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ORCIDBernhard Schölkopf
Person information
- affiliation: Max Planck Institute for Intelligent Systems, Tübingen, Germany
- award (2018): Gottfried Wilhelm Leibniz Prize
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Books and Theses
- 2002
[b2]Bernhard Schölkopf, Alexander Johannes Smola:
Learning with Kernels: support vector machines, regularization, optimization, and beyond. Adaptive computation and machine learning series, MIT Press 2002, ISBN 9780262194754, pp. I-XVIII, 1-626- 1997
- [b1]Bernhard Schölkopf:
Support vector learning. Berlin Institute of Technology, Oldenbourg 1997, ISBN 3-486-24632-1, pp. 1-173
Journal Articles
- 2024
- [j122]Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach:
Deep Backtracking Counterfactuals for Causally Compliant Explanations. Trans. Mach. Learn. Res. 2024 (2024) - [j121]Soledad Villar, David W. Hogg, Weichi Yao, George A. Kevrekidis, Bernhard Schölkopf:
Towards fully covariant machine learning. Trans. Mach. Learn. Res. 2024 (2024) - 2023
- [j120]Hao Ma, Dieter Büchler, Bernhard Schölkopf, Michael Muehlebach:
Reinforcement learning with model-based feedforward inputs for robotic table tennis. Auton. Robots 47(8): 1387-1403 (2023) - [j119]Amir-Hossein Karimi, Gilles Barthe
, Bernhard Schölkopf, Isabel Valera:
A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations. ACM Comput. Surv. 55(5): 95:1-95:29 (2023) - [j118]Felix Laumann, Julius von Kügelgen, Junhyung Park, Bernhard Schölkopf, Mauricio Barahona:
Kernel-Based Independence Tests for Causal Structure Learning on Functional Data. Entropy 25(12): 1597 (2023) - [j117]Carl-Johann Simon-Gabriel, Alessandro Barp, Bernhard Schölkopf, Lester Mackey:
Metrizing Weak Convergence with Maximum Mean Discrepancies. J. Mach. Learn. Res. 24: 184:1-184:20 (2023) - [j116]Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato:
normflows: A PyTorch Package for Normalizing Flows. J. Open Source Softw. 8(87): 5361 (2023) - [j115]R. Patrick Xian, Vincent Stimper, Marios Zacharias, Maciej Dendzik, Shuo Dong, Samuel Beaulieu, Bernhard Schölkopf, Martin Wolf, Laurenz Rettig, Christian Carbogno, Stefan Bauer, Ralph Ernstorfer:
A machine learning route between band mapping and band structure. Nat. Comput. Sci. 3(1): 101-114 (2023) - [j114]Armin Kekic, Jonas Dehning, Luigi Gresele, Julius von Kügelgen, Viola Priesemann, Bernhard Schölkopf:
Evaluating vaccine allocation strategies using simulation-assisted causal modeling. Patterns 4(6): 100739 (2023) - [j113]Arash Mehrjou, Ashkan Soleymani, Amin Abyaneh, Samir Bhatt, Bernhard Schölkopf, Stefan Bauer:
Pyfectious: An individual-level simulator to discover optimal containment policies for epidemic diseases. PLoS Comput. Biol. 19(1) (2023) - [j112]Olga Mineeva, Daniel Danciu, Bernhard Schölkopf, Ruth E. Ley, Gunnar Rätsch, Nicholas D. Youngblut:
ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning. PLoS Comput. Biol. 19(5) (2023) - [j111]Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Bernhard Schölkopf, Michael Curtis Mozer, Christopher Pal, Yoshua Bengio:
Neural Causal Structure Discovery from Interventions. Trans. Mach. Learn. Res. 2023 (2023) - [j110]Anson Lei, Bernhard Schölkopf, Ingmar Posner:
Variational Causal Dynamics: Discovering Modular World Models from Interventions. Trans. Mach. Learn. Res. 2023 (2023) - [j109]Patrik Reizinger, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel:
Jacobian-based Causal Discovery with Nonlinear ICA. Trans. Mach. Learn. Res. 2023 (2023) - 2022
- [j108]Lukas Kondmann, Aysim Toker, Sudipan Saha, Bernhard Schölkopf, Laura Leal-Taixé, Xiao Xiang Zhu:
Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images. IEEE Trans. Geosci. Remote. Sens. 60: 1-15 (2022) - [j107]Ashkan Soleymani, Anant Raj, Stefan Bauer, Bernhard Schölkopf, Michel Besserve:
Causal Feature Selection via Orthogonal Search. Trans. Mach. Learn. Res. 2022 (2022) - [j106]Dieter Büchler, Simon Guist, Roberto Calandra, Vincent Berenz, Bernhard Schölkopf, Jan Peters:
Learning to Play Table Tennis From Scratch Using Muscular Robots. IEEE Trans. Robotics 38(6): 3850-3860 (2022) - [j105]Lars Lorch, Heiner Kremer, William Trouleau, Stratis Tsirtsis, Aron Szanto, Bernhard Schölkopf, Manuel Gomez-Rodriguez:
Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots. ACM Trans. Spatial Algorithms Syst. 8(4): 25:1-25:28 (2022) - 2021
- [j104]Tobias Hepp, Dominik Blum, Karim Armanious, Bernhard Schölkopf, Darko Stern, Bin Yang, Sergios Gatidis:
Uncertainty estimation and explainability in deep learning-based age estimation of the human brain: Results from the German National Cohort MRI study. Comput. Medical Imaging Graph. 92: 101967 (2021) - [j103]Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, Yoshua Bengio:
Toward Causal Representation Learning. Proc. IEEE 109(5): 612-634 (2021) - [j102]Julius von Kügelgen, Luigi Gresele, Bernhard Schölkopf:
Simpson's Paradox in COVID-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects. IEEE Trans. Artif. Intell. 2(1): 18-27 (2021) - [j101]Samuel Bustamante, Jan Peters, Bernhard Schölkopf, Moritz Grosse-Wentrup, Vinay Jayaram:
ArmSym: A Virtual Human-Robot Interaction Laboratory for Assistive Robotics. IEEE Trans. Hum. Mach. Syst. 51(6): 568-577 (2021) - 2020
- [j100]Olga Mineeva, Mateo Rojas-Carulla, Ruth E. Ley, Bernhard Schölkopf, Nicholas D. Youngblut:
DeepMAsED: evaluating the quality of metagenomic assemblies. Bioinform. 36(10): 3011-3017 (2020) - [j99]Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf:
Causal Discovery from Heterogeneous/Nonstationary Data. J. Mach. Learn. Res. 21: 89:1-89:53 (2020) - [j98]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. J. Mach. Learn. Res. 21: 209:1-209:62 (2020) - [j97]Sebastián Gómez-González, Sergey Prokudin, Bernhard Schölkopf, Jan Peters:
Real Time Trajectory Prediction Using Deep Conditional Generative Models. IEEE Robotics Autom. Lett. 5(2): 970-976 (2020) - [j96]Sebastián Gómez-González, Gerhard Neumann, Bernhard Schölkopf, Jan Peters:
Adaptation and Robust Learning of Probabilistic Movement Primitives. IEEE Trans. Robotics 36(2): 366-379 (2020) - 2019
- [j95]Vincent Stimper, Stefan Bauer, Ralph Ernstorfer, Bernhard Schölkopf, Rui Patrick Xian:
Multidimensional Contrast Limited Adaptive Histogram Equalization. IEEE Access 7: 165437-165447 (2019) - [j94]Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf:
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise. J. Mach. Learn. Res. 20: 147:1-147:50 (2019) - [j93]Rohit Babbar, Bernhard Schölkopf:
Data scarcity, robustness and extreme multi-label classification. Mach. Learn. 108(8-9): 1329-1351 (2019) - [j92]Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf:
Analysis of cause-effect inference by comparing regression errors. PeerJ Comput. Sci. 5: e169 (2019) - [j91]Sebastián Gómez-González, Yassine Nemmour, Bernhard Schölkopf, Jan Peters:
Reliable Real-Time Ball Tracking for Robot Table Tennis. Robotics 8(4): 90 (2019) - 2018
- [j90]Mateo Rojas-Carulla, Bernhard Schölkopf, Richard E. Turner, Jonas Peters:
Invariant Models for Causal Transfer Learning. J. Mach. Learn. Res. 19: 36:1-36:34 (2018) - [j89]Carl-Johann Simon-Gabriel, Bernhard Schölkopf:
Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions. J. Mach. Learn. Res. 19: 44:1-44:29 (2018) - [j88]Dieter Buchler, Roberto Calandra, Bernhard Schölkopf, Jan Peters:
Control of Musculoskeletal Systems Using Learned Dynamics Models. IEEE Robotics Autom. Lett. 3(4): 3161-3168 (2018) - [j87]Lei Xiao, Felix Heide, Wolfgang Heidrich, Bernhard Schölkopf, Michael Hirsch:
Discriminative Transfer Learning for General Image Restoration. IEEE Trans. Image Process. 27(8): 4091-4104 (2018) - 2017
- [j86]Zhikun Wang, Abdeslam Boularias, Katharina Mülling, Bernhard Schölkopf, Jan Peters:
Anticipatory action selection for human-robot table tennis. Artif. Intell. 247: 399-414 (2017) - [j85]Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Bernhard Schölkopf:
Kernel Mean Embedding of Distributions: A Review and Beyond. Found. Trends Mach. Learn. 10(1-2): 1-141 (2017) - [j84]Mohammad Khatami, Tobias Schmidt-Wilcke, Pia C. Sundgren, Amin Abbasloo, Bernhard Schölkopf, Thomas Schultz:
BundleMAP: Anatomically localized classification, regression, and hypothesis testing in diffusion MRI. Pattern Recognit. 63: 593-600 (2017) - 2016
- [j83]Vinay Jayaram, Morteza Alamgir, Yasemin Altun, Bernhard Schölkopf, Moritz Grosse-Wentrup:
Transfer Learning in Brain-Computer Interfaces Abstract\uFFFDThe performance of brain-computer interfaces (BCIs) improves with the amount of avail. IEEE Comput. Intell. Mag. 11(1): 20-31 (2016) - [j82]Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf:
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks. J. Mach. Learn. Res. 17: 32:1-32:102 (2016) - [j81]Krikamol Muandet, Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Shrinkage Estimators. J. Mach. Learn. Res. 17: 48:1-48:41 (2016) - [j80]Manuel Gomez-Rodriguez, Le Song, Hadi Daneshmand, Bernhard Schölkopf:
Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm. J. Mach. Learn. Res. 17: 90:1-90:29 (2016) - [j79]Moritz Grosse-Wentrup, Dominik Janzing, Markus Siegel, Bernhard Schölkopf:
Identification of causal relations in neuroimaging data with latent confounders: An instrumental variable approach. NeuroImage 125: 825-833 (2016) - [j78]Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf:
Learning to Deblur. IEEE Trans. Pattern Anal. Mach. Intell. 38(7): 1439-1451 (2016) - [j77]Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters:
Modeling confounding by half-sibling regression. Proc. Natl. Acad. Sci. USA 113(27): 7391-7398 (2016) - [j76]Edgar D. Klenske, Melanie Nicole Zeilinger, Bernhard Schölkopf, Philipp Hennig:
Gaussian Process-Based Predictive Control for Periodic Error Correction. IEEE Trans. Control. Syst. Technol. 24(1): 110-121 (2016) - [j75]Kun Zhang, Zhikun Wang, Jiji Zhang, Bernhard Schölkopf:
On Estimation of Functional Causal Models: General Results and Application to the Post-Nonlinear Causal Model. ACM Trans. Intell. Syst. Technol. 7(2): 13:1-13:22 (2016) - [j74]Kun Zhang, Jiuyong Li, Elias Bareinboim, Bernhard Schölkopf, Judea Pearl:
Preface to the ACM TIST Special Issue on Causal Discovery and Inference. ACM Trans. Intell. Syst. Technol. 7(2): 17:1-17:3 (2016) - [j73]Manuel Gomez-Rodriguez, Le Song, Nan Du, Hongyuan Zha, Bernhard Schölkopf:
Influence Estimation and Maximization in Continuous-Time Diffusion Networks. ACM Trans. Inf. Syst. 34(2): 9:1-9:33 (2016) - 2015
- [j72]Dominik Janzing, Bernhard Schölkopf:
Semi-supervised interpolation in an anticausal learning scenario. J. Mach. Learn. Res. 16: 1923-1948 (2015) - [j71]Bernhard Schölkopf:
Artificial intelligence: Learning to see and act. Nat. 518(7540): 486-487 (2015) - [j70]Sebastian Weichwald, Timm Meyer, Ozan Özdenizci, Bernhard Schölkopf, Tonio Ball, Moritz Grosse-Wentrup:
Causal interpretation rules for encoding and decoding models in neuroimaging. NeuroImage 110: 48-59 (2015) - [j69]Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Stefan Harmeling, Jonas Peters:
Computing functions of random variables via reproducing kernel Hilbert space representations. Stat. Comput. 25(4): 755-766 (2015) - 2014
- [j68]Katharina Mülling, Abdeslam Boularias, Betty J. Mohler, Bernhard Schölkopf, Jan Peters:
Learning strategies in table tennis using inverse reinforcement learning. Biol. Cybern. 108(5): 603-619 (2014) - [j67]Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
Causal discovery with continuous additive noise models. J. Mach. Learn. Res. 15(1): 2009-2053 (2014) - [j66]Zhitang Chen, Kun Zhang, Laiwan Chan, Bernhard Schölkopf:
Causal Discovery via Reproducing Kernel Hilbert Space Embeddings. Neural Comput. 26(7): 1484-1517 (2014) - [j65]Manuel Gomez-Rodriguez, Jure Leskovec, David Balduzzi, Bernhard Schölkopf:
Uncovering the structure and temporal dynamics of information propagation. Netw. Sci. 2(1): 26-65 (2014) - [j64]Claudio Persello, Abdeslam Boularias, Michele Dalponte, Terje Gobakken, Erik Næsset, Bernhard Schölkopf:
Cost-Sensitive Active Learning With Lookahead: Optimizing Field Surveys for Remote Sensing Data Classification. IEEE Trans. Geosci. Remote. Sens. 52(10): 6652-6664 (2014) - 2013
- [j63]Thomas Schultz, Lara Schlaffke, Bernhard Schölkopf, Tobias Schmidt-Wilcke:
HiFiVE: A Hilbert Space Embedding of Fiber Variability Estimates for Uncertainty Modeling and Visualization. Comput. Graph. Forum 32(3): 121-130 (2013) - [j62]Zhikun Wang, Katharina Mülling, Marc Peter Deisenroth, Heni Ben Amor, David Vogt, Bernhard Schölkopf, Jan Peters:
Probabilistic movement modeling for intention inference in human-robot interaction. Int. J. Robotics Res. 32(7): 841-858 (2013) - 2012
- [j61]Dominik Janzing, Joris M. Mooij, Kun Zhang, Jan Lemeire, Jakob Zscheischler, Povilas Daniusis, Bastian Steudel, Bernhard Schölkopf:
Information-geometric approach to inferring causal directions. Artif. Intell. 182-183: 1-31 (2012) - [j60]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Two-Sample Test. J. Mach. Learn. Res. 13: 723-773 (2012) - 2011
- [j59]Michael Hirsch, Bernhard Schölkopf, Michael Habeck:
A Blind Deconvolution Approach for Improving the Resolution of Cryo-EM Density Maps. J. Comput. Biol. 18(3): 335-346 (2011) - [j58]Elisabeth Georgii, Koji Tsuda, Bernhard Schölkopf:
Multi-way set enumeration in weight tensors. Mach. Learn. 82(2): 123-155 (2011) - [j57]Suzanna Martens, Joris M. Mooij, N. Jeremy Hill, Jason Farquhar, Bernhard Schölkopf:
A Graphical Model Framework for Decoding in the Visual ERP-Based BCI Speller. Neural Comput. 23(1): 160-182 (2011) - [j56]Moritz Grosse-Wentrup, Bernhard Schölkopf, N. Jeremy Hill:
Causal influence of gamma oscillations on the sensorimotor rhythm. NeuroImage 56(2): 837-842 (2011) - [j55]Jonas Peters, Dominik Janzing, Bernhard Schölkopf:
Causal Inference on Discrete Data Using Additive Noise Models. IEEE Trans. Pattern Anal. Mach. Intell. 33(12): 2436-2450 (2011) - 2010
- [j54]Michel Besserve, Bernhard Schölkopf, Nikos K. Logothetis, Stefano Panzeri:
Causal relationships between frequency bands of extracellular signals in visual cortex revealed by an information theoretic analysis. J. Comput. Neurosci. 29(3): 547-566 (2010) - [j53]Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, Gert R. G. Lanckriet:
Hilbert Space Embeddings and Metrics on Probability Measures. J. Mach. Learn. Res. 11: 1517-1561 (2010) - [j52]Gustavo Camps-Valls, Joris M. Mooij, Bernhard Schölkopf:
Remote Sensing Feature Selection by Kernel Dependence Measures. IEEE Geosci. Remote. Sens. Lett. 7(3): 587-591 (2010) - [j51]Florian Steinke, Matthias Hein, Bernhard Schölkopf:
Nonparametric Regression between General Riemannian Manifolds. SIAM J. Imaging Sci. 3(3): 527-563 (2010) - [j50]Dominik Janzing, Bernhard Schölkopf:
Causal inference using the algorithmic Markov condition. IEEE Trans. Inf. Theory 56(10): 5168-5194 (2010) - 2009
- [j49]Hyunjung Shin, Koji Tsuda, Bernhard Schölkopf:
Protein functional class prediction with a combined graph. Expert Syst. Appl. 36(2): 3284-3292 (2009) - [j48]Arnulf B. A. Graf, Olivier Bousquet, Gunnar Rätsch, Bernhard Schölkopf:
Prototype Classification: Insights from Machine Learning. Neural Comput. 21(1): 272-300 (2009) - 2008
- [j47]Florian Steinke, Matthias Hein, Jan Peters, Bernhard Schölkopf:
Manifold-valued Thin-Plate Splines with Applications in Computer Graphics. Comput. Graph. Forum 27(2): 437-448 (2008) - [j46]William T. Freeman, Pietro Perona, Bernhard Schölkopf:
Guest Editorial. Int. J. Comput. Vis. 77(1-3): 1 (2008) - [j45]Xiaohai Sun, Dominik Janzing, Bernhard Schölkopf:
Causal reasoning by evaluating the complexity of conditional densities with kernel methods. Neurocomputing 71(7-9): 1248-1256 (2008) - [j44]Asa Ben-Hur, Cheng Soon Ong, Sören Sonnenburg, Bernhard Schölkopf, Gunnar Rätsch:
Support Vector Machines and Kernels for Computational Biology. PLoS Comput. Biol. 4(10) (2008) - [j43]Florian Steinke, Bernhard Schölkopf:
Kernels, regularization and differential equations. Pattern Recognit. 41(11): 3271-3286 (2008) - 2007
- [j42]Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Léon Bottou, Geoffrey Holmes, Yann LeCun, Klaus-Robert Müller, Fernando Pereira, Carl Edward Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Pascal Vincent, Jason Weston, Robert C. Williamson:
The Need for Open Source Software in Machine Learning. J. Mach. Learn. Res. 8: 2443-2466 (2007) - [j41]Gunnar Rätsch, Sören Sonnenburg, Jagan Srinivasan, Hanh Witte, Klaus-Robert Müller, Ralf J. Sommer, Bernhard Schölkopf:
Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning. PLoS Comput. Biol. 3(2) (2007) - [j40]Tobias Pfingsten, Daniel J. L. Herrmann, Thomas Schnitzler, Andreas Feustel, Bernhard Schölkopf:
Feature Selection for Troubleshooting in Complex Assembly Lines. IEEE Trans Autom. Sci. Eng. 4(3): 465-469 (2007) - [j39]Stephan Waldert, Michael Bensch, Martin Bogdan, Wolfgang Rosenstiel, Bernhard Schölkopf, Curtis L. Lowery, Hari Eswaran, Hubert Preissl:
Real-Time Fetal Heart Monitoring in Biomagnetic Measurements Using Adaptive Real-Time ICA. IEEE Trans. Biomed. Eng. 54(10): 1867-1874 (2007) - 2006
- [j38]Christian Walder, Bernhard Schölkopf, Olivier Chapelle:
Implicit Surface Modelling with a Globally Regularised Basis of Compact Support. Comput. Graph. Forum 25(3): 635-644 (2006) - [j37]Mingrui Wu, Bernhard Schölkopf, Gökhan H. Bakir:
A Direct Method for Building Sparse Kernel Learning Algorithms. J. Mach. Learn. Res. 7: 603-624 (2006) - [j36]Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer, Bernhard Schölkopf:
Large Scale Multiple Kernel Learning. J. Mach. Learn. Res. 7: 1531-1565 (2006) - [j35]Arnulf B. A. Graf, Felix A. Wichmann, Heinrich H. Bülthoff, Bernhard Schölkopf:
Classification of Faces in Man and Machine. Neural Comput. 18(1): 143-165 (2006) - [j34]Matthias O. Franz, Bernhard Schölkopf:
A Unifying View of Wiener and Volterra Theory and Polynomial Kernel Regression. Neural Comput. 18(12): 3097-3118 (2006) - 2005
- [j33]Florian Steinke, Bernhard Schölkopf, Volker Blanz:
Support Vector Machines for 3D Shape Processing. Comput. Graph. Forum 24(3): 285-294 (2005) - [j32]Michael Schröder, Thomas Navin Lal, Thilo Hinterberger, Martin Bogdan, N. Jeremy Hill, Niels Birbaumer, Wolfgang Rosenstiel, Bernhard Schölkopf:
Robust EEG Channel Selection across Subjects for Brain-Computer Interfaces. EURASIP J. Adv. Signal Process. 2005(19): 3103-3112 (2005) - [j31]Matthias Hein, Olivier Bousquet, Bernhard Schölkopf:
Maximal margin classification for metric spaces. J. Comput. Syst. Sci. 71(3): 333-359 (2005) - [j30]Arthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf:
Kernel Methods for Measuring Independence. J. Mach. Learn. Res. 6: 2075-2129 (2005) - [j29]Athanassia Chalimourda, Bernhard Schölkopf, Alexander J. Smola:
Experimentally optimal nu in support vector regression for different noise models and parameter settings. Neural Networks 18(2): 205- (2005) - [j28]Kwang In Kim, Matthias O. Franz, Bernhard Schölkopf:
Iterative Kernel Principal Component Analysis for Image Modeling. IEEE Trans. Pattern Anal. Mach. Intell. 27(9): 1351-1366 (2005) - 2004
- [j27]Holger Fröhlich, Olivier Chapelle, Bernhard Schölkopf:
Feature Selection for Support Vector Machines Using Genetic Algorithms. Int. J. Artif. Intell. Tools 13(4): 791-800 (2004) - [j26]Ulrike von Luxburg, Olivier Bousquet, Bernhard Schölkopf:
A Compression Approach to Support Vector Model Selection. J. Mach. Learn. Res. 5: 293-323 (2004) - [j25]Athanassia Chalimourda, Bernhard Schölkopf, Alexander J. Smola:
Experimentally optimal v in support vector regression for different noise models and parameter settings. Neural Networks 17(1): 127-141 (2004) - [j24]Alexander J. Smola, Bernhard Schölkopf:
A tutorial on support vector regression. Stat. Comput. 14(3): 199-222 (2004) - [j23]Thomas Navin Lal, Michael Schröder, Thilo Hinterberger, Jason Weston, Martin Bogdan, Niels Birbaumer, Bernhard Schölkopf:
Support vector channel selection in BCI. IEEE Trans. Biomed. Eng. 51(6): 1003-1010 (2004) - 2003
- [j22]Jason Weston, Fernando Pérez-Cruz, Olivier Bousquet, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf:
Feature selection and transduction for prediction of molecular bioactivity for drug design. Bioinform. 19(6): 764-771 (2003) - [j21]Bernhard Schölkopf:
Statistical learning theory, capacity, and complexity. Complex. 8(4): 87-94 (2003) - [j20]Jason Weston, André Elisseeff, Bernhard Schölkopf, Michael E. Tipping:
Use of the Zero-Norm with Linear Models and Kernel Methods. J. Mach. Learn. Res. 3: 1439-1461 (2003) - [j19]Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller:
Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces. IEEE Trans. Pattern Anal. Mach. Intell. 25(5): 623-633 (2003) - 2002
- [j18]Nello Cristianini, Bernhard Schölkopf:
Support Vector Machines and Kernel Methods: The New Generation of Learning Machines. AI Mag. 23(3): 31-42 (2002) - [j17]Dennis DeCoste, Bernhard Schölkopf:
Training Invariant Support Vector Machines. Mach. Learn. 46(1-3): 161-190 (2002) - [j16]Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Klaus-Robert Müller:
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification. IEEE Trans. Pattern Anal. Mach. Intell. 24(9): 1184-1199 (2002) - 2001
- [j15]Alexander J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson:
Regularized Principal Manifolds. J. Mach. Learn. Res. 1: 179-209 (2001) - [j14]Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alexander J. Smola, Robert C. Williamson:
Estimating the Support of a High-Dimensional Distribution. Neural Comput. 13(7): 1443-1471 (2001) - [j13]Robert C. Williamson, Alexander J. Smola, Bernhard Schölkopf:
Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators. IEEE Trans. Inf. Theory 47(6): 2516-2532 (2001) - [j12]Klaus-Robert Müller, Sebastian Mika, Gunnar Rätsch, Koji Tsuda, Bernhard Schölkopf:
An introduction to kernel-based learning algorithms. IEEE Trans. Neural Networks 12(2): 181-201 (2001) - 2000
- [j11]Alexander Zien, Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Thomas Lengauer, Klaus-Robert Müller:
Engineering support vector machine kernels that recognize translation initiation sites. Bioinform. 16(9): 799-807 (2000) - [j10]Bernhard Schölkopf, Alexander J. Smola, Robert C. Williamson, Peter L. Bartlett:
New Support Vector Algorithms. Neural Comput. 12(5): 1207-1245 (2000) - 1999
- [j9]Bernhard Schölkopf, Klaus-Robert Müller, Alexander J. Smola:
Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten. Inform. Forsch. Entwickl. 14(3): 154-163 (1999) - [j8]Bernhard Schölkopf, Sebastian Mika, Christopher J. C. Burges, Phil Knirsch, Klaus-Robert Müller, Gunnar Rätsch, Alexander J. Smola:
Input space versus feature space in kernel-based methods. IEEE Trans. Neural Networks 10(5): 1000-1017 (1999) - 1998
- [j7]Alexander J. Smola, Bernhard Schölkopf:
On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion. Algorithmica 22(1/2): 211-231 (1998) - [j6]Matthias O. Franz, Bernhard Schölkopf, Hanspeter A. Mallot, Heinrich H. Bülthoff:
Learning View Graphs for Robot Navigation. Auton. Robots 5(1): 111-125 (1998) - [j5]Matthias O. Franz, Bernhard Schölkopf, Hanspeter A. Mallot, Heinrich H. Bülthoff:
Where did I take that snapshot? Scene-based homing by image matching. Biol. Cybern. 79(3): 191-202 (1998) - [j4]Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller:
Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Comput. 10(5): 1299-1319 (1998) - [j3]Alexander J. Smola, Bernhard Schölkopf, Klaus-Robert Müller:
The connection between regularization operators and support vector kernels. Neural Networks 11(4): 637-649 (1998) - 1997
- [j2]Bernhard Schölkopf, Kah Kay Sung, Christopher J. C. Burges, Federico Girosi, Partha Niyogi, Tomaso A. Poggio, Vladimir Vapnik:
Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans. Signal Process. 45(11): 2758-2765 (1997) - 1995
- [j1]Bernhard Schölkopf, Hanspeter A. Mallot:
View-Based Cognitive Mapping and Path Planning. Adapt. Behav. 3(3): 311-348 (1995)
Conference and Workshop Papers
- 2024
- [c448]Flavio Schneider, Ojasv Kamal, Zhijing Jin, Bernhard Schölkopf:
Moûsai: Efficient Text-to-Music Diffusion Models. ACL (1) 2024: 8050-8068 - [c447]Ishan Agrawal, Zhijing Jin, Ehsan Mokhtarian, Siyuan Guo, Yuen Chen, Mrinmaya Sachan, Bernhard Schölkopf:
CausalCite: A Causal Formulation of Paper Citations. ACL (Findings) 2024: 8395-8410 - [c446]Francesco Ortu, Zhijing Jin, Diego Doimo, Mrinmaya Sachan, Alberto Cazzaniga, Bernhard Schölkopf:
Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals. ACL (1) 2024: 8420-8436 - [c445]Lars Lorch, Andreas Krause, Bernhard Schölkopf:
Causal Modeling with Stationary Diffusions. AISTATS 2024: 1927-1935 - [c444]Gege Gao, Weiyang Liu, Anpei Chen, Andreas Geiger, Bernhard Schölkopf:
GraphDreamer: Compositional 3D Scene Synthesis from Scene Graphs. CVPR 2024: 21295-21304 - [c443]Siyuan Guo, Jonas Bernhard Wildberger, Bernhard Schölkopf:
Out-of-Variable Generalisation for Discriminative Models. ICLR 2024 - [c442]Zhijing Jin, Jiarui Liu, Zhiheng Lyu, Spencer Poff, Mrinmaya Sachan, Rada Mihalcea, Mona T. Diab, Bernhard Schölkopf:
Can Large Language Models Infer Causation from Correlation? ICLR 2024 - [c441]Zhen Liu, Yao Feng, Yuliang Xiu, Weiyang Liu, Liam Paull, Michael J. Black, Bernhard Schölkopf:
Ghost on the Shell: An Expressive Representation of General 3D Shapes. ICLR 2024 - [c440]Weiyang Liu, Zeju Qiu, Yao Feng, Yuliang Xiu, Yuxuan Xue, Longhui Yu, Haiwen Feng, Zhen Liu, Juyeon Heo, Songyou Peng, Yandong Wen, Michael J. Black, Adrian Weller, Bernhard Schölkopf:
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization. ICLR 2024 - [c439]Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Rätsch, Guy Tennenholtz:
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding. ICLR 2024 - [c438]Hsiao-Ru Pan, Bernhard Schölkopf:
Skill or Luck? Return Decomposition via Advantage Functions. ICLR 2024 - [c437]Jan Schneider, Pierre Schumacher, Simon Guist, Le Chen, Daniel F. B. Haeufle, Bernhard Schölkopf, Dieter Büchler:
Identifying Policy Gradient Subspaces. ICLR 2024 - [c436]Aaron Spieler, Nasim Rahaman, Georg Martius, Bernhard Schölkopf, Anna Levina:
The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks. ICLR 2024 - [c435]Simon Buchholz, Bernhard Schölkopf:
Robustness of Nonlinear Representation Learning. ICML 2024 - [c434]Yaxi Hu, Amartya Sanyal, Bernhard Schölkopf:
Provable Privacy with Non-Private Pre-Processing. ICML 2024 - [c433]Heiner Kremer, Bernhard Schölkopf:
Geometry-Aware Instrumental Variable Regression. ICML 2024 - [c432]Andreas Opedal, Alessandro Stolfo, Haruki Shirakami, Ying Jiao, Ryan Cotterell, Bernhard Schölkopf, Abulhair Saparov, Mrinmaya Sachan:
Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners? ICML 2024 - [c431]Yujia Zheng, Zeyu Tang, Yiwen Qiu, Bernhard Schölkopf, Kun Zhang:
Detecting and Identifying Selection Structure in Sequential Data. ICML 2024 - [c430]Abby O'Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alexander Herzog, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew Wang, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Beomjoon Kim, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu, Charlotte Le, Chelsea Finn, Chen Wang, Chenfeng Xu, Cheng Chi, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Driess, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Paul Foster, Fangchen Liu, Federico Ceola, Fei Xia, Feiyu Zhao, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Ge Yan, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guanzhi Wang, Hao Su, Haoshu Fang, Haochen Shi, Henghui Bao, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim, Jaimyn Drake, Jan Peters, Jan Schneider, Jasmine Hsu, Jeannette Bohg, Jeffrey Bingham, Jeffrey Wu, Jensen Gao, Jiaheng Hu, Jiajun Wu, Jialin Wu, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jingyun Yang, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kaiyuan Wang, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Ken Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Lin, Kevin Zhang, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Lawrence Yunliang Chen, Lerrel Pinto, Li Fei-Fei, Liam Tan, Linxi Jim Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen, Nicolas Heess, Nikhil J. Joshi, Niko Sünderhauf, Ning Liu, Norman Di Palo, Nur Muhammad (Mahi) Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R. Sanketi, Patrick Tree Miller, Patrick Yin, Paul Wohlhart, Peng Xu, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Quan Vuong, Rafael Rafailov, Ran Tian, Ria Doshi, Roberto Martín-Martín, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Ruohan Zhang, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante, Sean Kirmani, Sergey Levine, Shan Lin, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham D. Sonawani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vincent Vanhoucke, Wei Zhan, Wenxuan Zhou, Wolfram Burgard, Xi Chen, Xiaolong Wang, Xinghao Zhu, Xinyang Geng, Xiyuan Liu, Liangwei Xu, Xuanlin Li, Yao Lu, Yecheng Jason Ma, Yejin Kim, Yevgen Chebotar, Yifan Zhou, Yifeng Zhu, Yilin Wu, Ying Xu, Yixuan Wang, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yue Cao, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zhuo Xu, Zichen Jeff Cui, Zichen Zhang, Zipeng Lin:
Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration. ICRA 2024: 6892-6903 - [c429]Felix Leeb, Bernhard Schölkopf:
A diverse Multilingual News Headlines Dataset from around the World. NAACL (Short Papers) 2024: 647-652 - [c428]Zhijing Jin, Yuen Chen, Fernando Gonzalez Adauto, Jiarui Liu, Jiayi Zhang, Julian Michael, Bernhard Schölkopf, Mona T. Diab:
Analyzing the Role of Semantic Representations in the Era of Large Language Models. NAACL-HLT 2024: 3781-3798 - [c427]Jonathan Thomm, Michael Hersche, Giacomo Camposampiero, Aleksandar Terzic, Bernhard Schölkopf, Abbas Rahimi:
Terminating Differentiable Tree Experts. NeSy (1) 2024: 296-311 - 2023
- [c426]Alessandro Stolfo, Zhijing Jin, Kumar Shridhar, Bernhard Schölkopf, Mrinmaya Sachan:
A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models. ACL (1) 2023: 545-561 - [c425]Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schölkopf, Mrinmaya Sachan, Taylor Berg-Kirkpatrick:
Membership Inference Attacks against Language Models via Neighbourhood Comparison. ACL (Findings) 2023: 11330-11343 - [c424]Alexander Hägele, Jonas Rothfuss, Lars Lorch, Vignesh Ram Somnath, Bernhard Schölkopf, Andreas Krause:
BaCaDI: Bayesian Causal Discovery with Unknown Interventions. AISTATS 2023: 1411-1436 - [c423]Zeju Qiu, Weiyang Liu, Tim Z. Xiao, Zhen Liu, Umang Bhatt, Yucen Luo, Adrian Weller, Bernhard Schölkopf:
Iterative Teaching by Data Hallucination. AISTATS 2023: 9892-9913 - [c422]Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence:
Dataflow graphs as complete causal graphs. CAIN 2023: 7-12 - [c421]Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf:
Unsupervised Object Learning via Common Fate. CLeaR 2023: 281-327 - [c420]Jonas Bernhard Wildberger, Siyuan Guo, Arnab Bhattacharyya, Bernhard Schölkopf:
On the Interventional Kullback-Leibler Divergence. CLeaR 2023: 328-349 - [c419]Yuejiang Liu, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow, Bernhard Schölkopf, Francesco Locatello:
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning. CLeaR 2023: 553-573 - [c418]Fernando Gonzalez Adauto, Zhijing Jin, Bernhard Schölkopf, Tom Hope, Mrinmaya Sachan, Rada Mihalcea:
Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good. EMNLP (Findings) 2023: 415-438 - [c417]Ahmad-Reza Ehyaei, Amir-Hossein Karimi, Bernhard Schölkopf, Setareh Maghsudi:
Robustness Implies Fairness in Causal Algorithmic Recourse. FAccT 2023: 984-1001 - [c416]Max-Olivier Van Bastelaer, Heiner Kremer, Valentin Volchkov, Jean-Claude Passy, Bernhard Schölkopf:
Glare Removal for Astronomical Images with High Local Dynamic Range. ICCP 2023: 1-11 - [c415]Yandong Wen, Weiyang Liu, Yao Feng, Bhiksha Raj, Rita Singh, Adrian Weller, Michael J. Black, Bernhard Schölkopf:
Pairwise Similarity Learning is SimPLE. ICCV 2023: 5285-5295 - [c414]Cian Eastwood, Andrei Liviu Nicolicioiu, Julius von Kügelgen, Armin Kekic, Frederik Träuble, Andrea Dittadi, Bernhard Schölkopf:
DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability. ICLR 2023 - [c413]Nico Gürtler, Sebastian Blaes, Pavel Kolev, Felix Widmaier, Manuel Wuthrich, Stefan Bauer, Bernhard Schölkopf, Georg Martius:
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware. ICLR 2023 - [c412]Felix Leeb, Giulia Lanzillotta, Yashas Annadani, Michel Besserve, Stefan Bauer, Bernhard Schölkopf:
Structure by Architecture: Structured Representations without Regularization. ICLR 2023 - [c411]Weiyang Liu, Longhui Yu, Adrian Weller, Bernhard Schölkopf:
Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap. ICLR 2023 - [c410]Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, Bernhard Schölkopf, José Miguel Hernández-Lobato:
Flow Annealed Importance Sampling Bootstrap. ICLR 2023 - [c409]Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel, Tong He, Zheng Zhang, Bernhard Schölkopf, Thomas Brox, Francesco Locatello:
Bridging the Gap to Real-World Object-Centric Learning. ICLR 2023 - [c408]Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel:
Provably Learning Object-Centric Representations. ICML 2023: 3038-3062 - [c407]Ricardo Dominguez-Olmedo, Amir-Hossein Karimi, Georgios Arvanitidis, Bernhard Schölkopf:
On Data Manifolds Entailed by Structural Causal Models. ICML 2023: 8188-8201 - [c406]Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx:
On the Identifiability and Estimation of Causal Location-Scale Noise Models. ICML 2023: 14316-14332 - [c405]Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf:
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. ICML 2023: 14333-14352 - [c404]Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim:
On the Relationship Between Explanation and Prediction: A Causal View. ICML 2023: 15861-15883 - [c403]Hamza Keurti, Hsiao-Ru Pan, Michel Besserve, Benjamin F. Grewe, Bernhard Schölkopf:
Homomorphism AutoEncoder - Learning Group Structured Representations from Observed Transitions. ICML 2023: 16190-16215 - [c402]Heiner Kremer, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu:
Estimation Beyond Data Reweighting: Kernel Method of Moments. ICML 2023: 17745-17783 - [c401]Sarthak Mittal, Korbinian Abstreiter, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou:
Diffusion Based Representation Learning. ICML 2023: 24963-24982 - [c400]Sidak Pal Singh, Thomas Hofmann, Bernhard Schölkopf:
The Hessian perspective into the Nature of Convolutional Neural Networks. ICML 2023: 31930-31968 - [c399]Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Curtis Mozer, Kenji Kawaguchi, Yoshua Bengio, Bernhard Schölkopf:
Discrete Key-Value Bottleneck. ICML 2023: 34431-34455 - [c398]Alexander Dittrich, Jan Schneider, Simon Guist, Nico Gürtler, Heiko Ott, Thomas Steinbrenner, Bernhard Schölkopf, Dieter Büchler:
AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation. ICRA 2023: 3058-3064 - [c397]Philip Tobuschat, Hao Ma, Dieter Büchler, Bernhard Schölkopf, Michael Muehlebach:
Data-Efficient Online Learning of Ball Placement in Robot Table Tennis. IROS 2023: 567-573 - [c396]Majid Khadiv, Avadesh Meduri, Huaijiang Zhu, Ludovic Righetti, Bernhard Schölkopf:
Learning Locomotion Skills from MPC in Sensor Space. L4DC 2023: 1218-1230 - [c395]Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar:
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. NeurIPS 2023 - [c394]Cian Eastwood, Shashank Singh, Andrei Liviu Nicolicioiu, Marin Vlastelica Pogancic, Julius von Kügelgen, Bernhard Schölkopf:
Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features. NeurIPS 2023 - [c393]Marco Fumero, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, Francesco Locatello:
Leveraging sparse and shared feature activations for disentangled representation learning. NeurIPS 2023 - [c392]Siyuan Guo, Viktor Tóth, Bernhard Schölkopf, Ferenc Huszar:
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data. NeurIPS 2023 - [c391]Zhijing Jin, Yuen Chen, Felix Leeb, Luigi Gresele, Ojasv Kamal, Zhiheng Lyu, Kevin Blin, Fernando Gonzalez Adauto, Max Kleiman-Weiner, Mrinmaya Sachan, Bernhard Schölkopf:
CLadder: A Benchmark to Assess Causal Reasoning Capabilities of Language Models. NeurIPS 2023 - [c390]Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf:
Nonparametric Identifiability of Causal Representations from Unknown Interventions. NeurIPS 2023 - [c389]Wendong Liang, Armin Kekic, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, Bernhard Schölkopf:
Causal Component Analysis. NeurIPS 2023 - [c388]Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato:
SE(3) Equivariant Augmented Coupling Flows. NeurIPS 2023 - [c387]Junhyung Park, Simon Buchholz, Bernhard Schölkopf, Krikamol Muandet:
A Measure-Theoretic Axiomatisation of Causality. NeurIPS 2023 - [c386]Zeju Qiu, Weiyang Liu, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, Bernhard Schölkopf:
Controlling Text-to-Image Diffusion by Orthogonal Finetuning. NeurIPS 2023 - [c385]Jonas Wildberger, Maximilian Dax, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf:
Flow Matching for Scalable Simulation-Based Inference. NeurIPS 2023 - [c384]Simon Guist, Jan Schneider, Vincent Berenz, Alexander Dittrich, Bernhard Schölkopf, Dieter Büchler:
Hindsight States: Blending Sim & Real Task Elements for Efficient Reinforcement Learning. Robotics: Science and Systems 2023 - [c383]Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach:
Causal effect estimation from observational and interventional data through matrix weighted linear estimators. UAI 2023: 1087-1097 - 2022
- [c382]Julius von Kügelgen, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller, Bernhard Schölkopf:
On the Fairness of Causal Algorithmic Recourse. AAAI 2022: 9584-9594 - [c381]Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet:
A Witness Two-Sample Test. AISTATS 2022: 1403-1419 - [c380]Georgios Arvanitidis, Bogdan M. Georgiev, Bernhard Schölkopf:
A prior-based approximate latent Riemannian metric. AISTATS 2022: 4634-4658 - [c379]Vincent Stimper, Bernhard Schölkopf, José Miguel Hernández-Lobato:
Resampling Base Distributions of Normalizing Flows. AISTATS 2022: 4915-4936 - [c378]Jia-Jie Zhu, Christina Kouridi, Yassine Nemmour, Bernhard Schölkopf:
Adversarially Robust Kernel Smoothing. AISTATS 2022: 4972-4994 - [c377]Sumedh A. Sontakke, Stephen Iota, Zizhao Hu, Arash Mehrjou, Laurent Itti, Bernhard Schölkopf:
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL. AISTATS 2022: 7518-7530 - [c376]Diego Agudelo-España, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu:
Learning Random Feature Dynamics for Uncertainty Quantification. CDC 2022: 4937-4944 - [c375]Yassine Nemmour, Heiner Kremer, Bernhard Schölkopf, Jia-Jie Zhu:
Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee. CDC 2022: 5660-5667 - [c374]Michel Besserve, Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf:
Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations. CLeaR 2022: 110-143 - [c373]Hanlin Zhang, Yi-Fan Zhang, Weiyang Liu, Adrian Weller, Bernhard Schölkopf, Eric P. Xing:
Towards Principled Disentanglement for Domain Generalization. CVPR 2022: 8014-8024 - [c372]Dominik Zietlow, Michael Lohaus, Guha Balakrishnan, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Chris Russell:
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers. CVPR 2022: 10400-10411 - [c371]Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, Peter V. Gehler:
Towards Total Recall in Industrial Anomaly Detection. CVPR 2022: 14298-14308 - [c370]Weiyang Liu, Zhen Liu, Liam Paull, Adrian Weller, Bernhard Schölkopf:
Structural Causal 3D Reconstruction. ECCV (1) 2022: 140-159 - [c369]Justus Mattern, Zhijing Jin, Benjamin Weggenmann, Bernhard Schölkopf, Mrinmaya Sachan:
Differentially Private Language Models for Secure Data Sharing. EMNLP 2022: 4860-4873 - [c368]Zhijing Jin, Abhinav Lalwani, Tejas Vaidhya, Xiaoyu Shen, Yiwen Ding, Zhiheng Lyu, Mrinmaya Sachan, Rada Mihalcea, Bernhard Schölkopf:
Logical Fallacy Detection. EMNLP (Findings) 2022: 7180-7198 - [c367]Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, Jakob H. Macke:
Group equivariant neural posterior estimation. ICLR 2022 - [c366]Cian Eastwood, Ian Mason, Christopher K. I. Williams, Bernhard Schölkopf:
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration. ICLR 2022 - [c365]Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf:
Invariant Causal Representation Learning for Out-of-Distribution Generalization. ICLR 2022 - [c364]Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf:
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. ICLR 2022 - [c363]Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter Vincent Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel:
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. ICLR 2022 - [c362]Sidak Pal Singh, Aurélien Lucchi, Thomas Hofmann, Bernhard Schölkopf:
Phenomenology of Double Descent in Finite-Width Neural Networks. ICLR 2022 - [c361]Frederik Träuble, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer:
The Role of Pretrained Representations for the OOD Generalization of RL Agents. ICLR 2022 - [c360]Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang:
Adversarial Robustness Through the Lens of Causality. ICLR 2022 - [c359]Andrea Dittadi, Samuele S. Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, Francesco Locatello:
Generalization and Robustness Implications in Object-Centric Learning. ICML 2022: 5221-5285 - [c358]Ricardo Dominguez-Olmedo, Amir-Hossein Karimi, Bernhard Schölkopf:
On the Adversarial Robustness of Causal Algorithmic Recourse. ICML 2022: 5324-5342 - [c357]Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing:
Causal Inference Through the Structural Causal Marginal Problem. ICML 2022: 7793-7824 - [c356]Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang:
Action-Sufficient State Representation Learning for Control with Structural Constraints. ICML 2022: 9260-9279 - [c355]Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf:
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions. ICML 2022: 11665-11682 - [c354]Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Dominik Janzing, Bernhard Schölkopf, Francesco Locatello:
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models. ICML 2022: 18741-18753 - [c353]Jingwei Ni, Zhijing Jin, Markus Freitag, Mrinmaya Sachan, Bernhard Schölkopf:
Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance. NAACL-HLT 2022: 5303-5320 - [c352]Simon Buchholz, Michel Besserve, Bernhard Schölkopf:
Function Classes for Identifiable Nonlinear Independent Component Analysis. NeurIPS 2022 - [c351]Aniket Das, Bernhard Schölkopf, Michael Muehlebach:
Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization. NeurIPS 2022 - [c350]Cian Eastwood, Alexander Robey, Shashank Singh, Julius von Kügelgen, Hamed Hassani, George J. Pappas, Bernhard Schölkopf:
Probable Domain Generalization via Quantile Risk Minimization. NeurIPS 2022 - [c349]Zhijing Jin, Sydney Levine, Fernando Gonzalez Adauto, Ojasv Kamal, Maarten Sap, Mrinmaya Sachan, Rada Mihalcea, Josh Tenenbaum, Bernhard Schölkopf:
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment. NeurIPS 2022 - [c348]Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf:
AutoML Two-Sample Test. NeurIPS 2022 - [c347]Felix Leeb, Stefan Bauer, Michel Besserve, Bernhard Schölkopf:
Exploring the Latent Space of Autoencoders with Interventional Assays. NeurIPS 2022 - [c346]Lars Lorch, Scott Sussex, Jonas Rothfuss, Andreas Krause, Bernhard Schölkopf:
Amortized Inference for Causal Structure Learning. NeurIPS 2022 - [c345]Hsiao-Ru Pan, Nico Gürtler, Alexander Neitz, Bernhard Schölkopf:
Direct Advantage Estimation. NeurIPS 2022 - [c344]Ronan Perry, Julius von Kügelgen, Bernhard Schölkopf:
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis. NeurIPS 2022 - [c343]Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve:
Embrace the Gap: VAEs Perform Independent Mechanism Analysis. NeurIPS 2022 - [c342]Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer:
Interventions, Where and How? Experimental Design for Causal Models at Scale. NeurIPS 2022 - [c341]Martin Weiss, Nasim Rahaman, Francesco Locatello, Chris Pal, Yoshua Bengio, Bernhard Schölkopf, Li Erran Li, Nicolas Ballas:
Neural Attentive Circuits. NeurIPS 2022 - [c340]Florian Wenzel, Andrea Dittadi, Peter V. Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, Francesco Locatello:
Assaying Out-Of-Distribution Generalization in Transfer Learning. NeurIPS 2022 - [c339]Hao Ma, Dieter Büchler, Bernhard Schölkopf, Michael Muehlebach:
A Learning-based Iterative Control Framework for Controlling a Robot Arm with Pneumatic Artificial Muscles. Robotics: Science and Systems 2022 - [c338]Michel Besserve, Bernhard Schölkopf:
Learning soft interventions in complex equilibrium systems. UAI 2022: 170-180 - 2021
- [c337]Michel Besserve, Rémy Sun, Dominik Janzing, Bernhard Schölkopf:
A Theory of Independent Mechanisms for Extrapolation in Generative Models. AAAI 2021: 6741-6749 - [c336]Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf:
Kernel Distributionally Robust Optimization: Generalized Duality Theorem and Stochastic Approximation. AISTATS 2021: 280-288 - [c335]Georgios Arvanitidis, Søren Hauberg, Bernhard Schölkopf:
Geometrically Enriched Latent Spaces. AISTATS 2021: 631-639 - [c334]Weiyang Liu, Rongmei Lin, Zhen Liu, Li Xiong, Bernhard Schölkopf, Adrian Weller:
Learning with Hyperspherical Uniformity. AISTATS 2021: 1180-1188 - [c333]Zhijing Jin, Zeyu Peng, Tejas Vaidhya, Bernhard Schölkopf, Rada Mihalcea:
Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID-19 Policies across the US States. EMNLP (Findings) 2021: 288-301 - [c332]Zhijing Jin, Julius von Kügelgen, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, Bernhard Schölkopf:
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP. EMNLP (1) 2021: 9499-9513 - [c331]Amir-Hossein Karimi, Bernhard Schölkopf, Isabel Valera:
Algorithmic Recourse: from Counterfactual Explanations to Interventions. FAccT 2021: 353-362 - [c330]Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Manuel Wuthrich, Yoshua Bengio, Bernhard Schölkopf, Stefan Bauer:
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning. ICLR 2021 - [c329]Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Benjamin Müller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilaniuk, David L. Buckeridge, Gaétan Marceau-Caron, Pierre Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Christopher J. Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams:
Predicting Infectiousness for Proactive Contact Tracing. ICLR 2021 - [c328]Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wuthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, Bernhard Schölkopf:
On the Transfer of Disentangled Representations in Realistic Settings. ICLR 2021 - [c327]Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, Bernhard Schölkopf:
Recurrent Independent Mechanisms. ICLR 2021 - [c326]Kanika Madan, Nan Rosemary Ke, Anirudh Goyal, Bernhard Schölkopf, Yoshua Bengio:
Fast And Slow Learning Of Recurrent Independent Mechanisms. ICLR 2021 - [c325]Alexander Neitz, Giambattista Parascandolo, Bernhard Schölkopf:
A teacher-student framework to distill future trajectories. ICLR 2021 - [c324]Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, Bernhard Schölkopf:
Learning explanations that are hard to vary. ICLR 2021 - [c323]Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf:
Spatially Structured Recurrent Modules. ICLR 2021 - [c322]Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis:
Bayesian Quadrature on Riemannian Data Manifolds. ICML 2021: 3459-3468 - [c321]Muhammad Waleed Gondal, Shruti Joshi, Nasim Rahaman, Stefan Bauer, Manuel Wuthrich, Bernhard Schölkopf:
Function Contrastive Learning of Transferable Meta-Representations. ICML 2021: 3755-3765 - [c320]Atalanti-Anastasia Mastakouri, Bernhard Schölkopf, Dominik Janzing:
Necessary and sufficient conditions for causal feature selection in time series with latent common causes. ICML 2021: 7502-7511 - [c319]Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet:
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression. ICML 2021: 8401-8412 - [c318]Sumedh A. Sontakke, Arash Mehrjou, Laurent Itti, Bernhard Schölkopf:
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning. ICML 2021: 9848-9858 - [c317]Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, Stefan Bauer:
On Disentangled Representations Learned from Correlated Data. ICML 2021: 10401-10412 - [c316]Arash Mehrjou, Mohammad Ghavamzadeh, Bernhard Schölkopf:
Neural Lyapunov Redesign. L4DC 2021: 459-470 - [c315]Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu:
Approximate Distributionally Robust Nonlinear Optimization with Application to Model Predictive Control: A Functional Approach. L4DC 2021: 1255-1269 - [c314]Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler:
Backward-Compatible Prediction Updates: A Probabilistic Approach. NeurIPS 2021: 116-128 - [c313]Nico Gürtler, Felix Widmaier, Cansu Sancaktar, Sebastian Blaes, Pavel Kolev, Stefan Bauer, Manuel Wüthrich, Markus Wulfmeier, Martin A. Riedmiller, Arthur Allshire, Qiang Wang, Robert McCarthy, Hangyeol Kim, Jongchan Baek, Wookyong Kwon, Shanliang Qian, Yasunori Toshimitsu, Mike Yan Michelis, Amirhossein Kazemipour, Arman Raayatsanati, Hehui Zheng, Barnabas Gavin Cangan, Bernhard Schölkopf, Georg Martius:
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World. NeurIPS (Competition and Demos) 2021: 133-150 - [c312]Stefan Bauer, Manuel Wüthrich, Felix Widmaier, Annika Buchholz, Sebastian Stark, Anirudh Goyal, Thomas Steinbrenner, Joel Akpo, Shruti Joshi, Vincent Berenz, Vaibhav Agrawal, Niklas Funk, Julen Urain De Jesus, Jan Peters, Joe Watson, Claire Chen, Krishnan Srinivasan, Junwu Zhang, Jeffrey Zhang, Matthew R. Walter, Rishabh Madan, Takuma Yoneda, Denis Yarats, Arthur Allshire, Ethan K. Gordon, Tapomayukh Bhattacharjee, Siddhartha S. Srinivasa, Animesh Garg, Takahiro Maeda, Harshit Sikchi, Jilong Wang, Qingfeng Yao, Shuyu Yang, Robert McCarthy, Francisco Roldan Sanchez, Qiang Wang, David Cordova Bulens, Kevin McGuinness, Noel E. O'Connor, Stephen J. Redmond, Bernhard Schölkopf:
Real Robot Challenge: A Robotics Competition in the Cloud. NeurIPS (Competition and Demos) 2021: 190-204 - [c311]Manuel Wüthrich, Bernhard Schölkopf, Andreas Krause:
Regret Bounds for Gaussian-Process Optimization in Large Domains. NeurIPS 2021: 7385-7396 - [c310]Nasim Rahaman, Muhammad Waleed Gondal, Shruti Joshi, Peter V. Gehler, Yoshua Bengio, Francesco Locatello, Bernhard Schölkopf:
Dynamic Inference with Neural Interpreters. NeurIPS 2021: 10985-10998 - [c309]Jonas M. Kübler, Simon Buchholz, Bernhard Schölkopf:
The Inductive Bias of Quantum Kernels. NeurIPS 2021: 12661-12673 - [c308]Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello:
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. NeurIPS 2021: 16451-16467 - [c307]Weiyang Liu, Zhen Liu, Hanchen Wang, Liam Paull, Bernhard Schölkopf, Adrian Weller:
Iterative Teaching by Label Synthesis. NeurIPS 2021: 21681-21695 - [c306]Maximilian Seitzer, Bernhard Schölkopf, Georg Martius:
Causal Influence Detection for Improving Efficiency in Reinforcement Learning. NeurIPS 2021: 22905-22918 - [c305]Lars Lorch, Jonas Rothfuss, Bernhard Schölkopf, Andreas Krause:
DiBS: Differentiable Bayesian Structure Learning. NeurIPS 2021: 24111-24123 - [c304]Luigi Gresele, Julius von Kügelgen, Vincent Stimper, Bernhard Schölkopf, Michel Besserve:
Independent mechanism analysis, a new concept? NeurIPS 2021: 28233-28248 - 2020
- [c303]Philippe Wenk, Gabriele Abbati, Michael A. Osborne, Bernhard Schölkopf, Andreas Krause, Stefan Bauer:
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems. AAAI 2020: 6364-6371 - [c302]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Commentary on the Unsupervised Learning of Disentangled Representations. AAAI 2020: 13681-13684 - [c301]Niki Kilbertus, Manuel Gomez Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera:
Fair Decisions Despite Imperfect Predictions. AISTATS 2020: 277-287 - [c300]Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf:
Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem. CDC 2020: 3457-3463 - [c299]Manuel Wuthrich, Felix Widmaier, Felix Grimminger, Shruti Joshi, Vaibhav Agrawal, Bilal Hammoud, Majid Khadiv, Miroslav Bogdanovic, Vincent Berenz, Julian Viereck, Maximilien Naveau, Ludovic Righetti, Bernhard Schölkopf, Stefan Bauer:
TriFinger: An Open-Source Robot for Learning Dexterity. CoRL 2020: 1871-1882 - [c298]Michel Besserve, Arash Mehrjou, Rémy Sun, Bernhard Schölkopf:
Counterfactuals uncover the modular structure of deep generative models. ICLR 2020 - [c297]Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael J. Black, Bernhard Schölkopf:
From Variational to Deterministic Autoencoders. ICLR 2020 - [c296]Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem:
Disentangling Factors of Variations Using Few Labels. ICLR 2020 - [c295]Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera:
Towards Causal Algorithmic Recourse. xxAI@ICML 2020: 139-166 - [c294]Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen:
Weakly-Supervised Disentanglement Without Compromises. ICML 2020: 6348-6359 - [c293]Diego Agudelo-España, Andrii Zadaianchuk, Philippe Wenk, Aditya Garg, Joel Akpo, Felix Grimminger, Julian Viereck, Maximilien Naveau, Ludovic Righetti, Georg Martius, Andreas Krause, Bernhard Schölkopf, Stefan Bauer, Manuel Wüthrich:
A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models. ICRA 2020: 8151-8157 - [c292]Jia-Jie Zhu, Bernhard Schölkopf, Moritz Diehl:
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control. L4DC 2020: 915-923 - [c291]Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvärinen:
Relative gradient optimization of the Jacobian term in unsupervised deep learning. NeurIPS 2020 - [c290]Amir-Hossein Karimi, Bodo Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera:
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach. NeurIPS 2020 - [c289]Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet:
Learning Kernel Tests Without Data Splitting. NeurIPS 2020 - [c288]Atalanti-Anastasia Mastakouri, Bernhard Schölkopf:
Causal analysis of Covid-19 Spread in Germany. NeurIPS 2020 - [c287]Julius von Kügelgen, Alexander Mey, Marco Loog, Bernhard Schölkopf:
Semi-supervised learning, causality, and the conditional cluster assumption. UAI 2020: 1-10 - [c286]Behzad Tabibian, Vicenç Gómez, Abir De, Bernhard Schölkopf, Manuel Gomez Rodriguez:
On the design of consequential ranking algorithms. UAI 2020: 171-180 - [c285]Wittawat Jitkrittum, Heishiro Kanagawa, Bernhard Schölkopf:
Testing Goodness of Fit of Conditional Density Models with Kernels. UAI 2020: 221-230 - [c284]Diego Agudelo-España, Sebastián Gómez-González, Stefan Bauer, Bernhard Schölkopf, Jan Peters:
Bayesian Online Prediction of Change Points. UAI 2020: 320-329 - [c283]Matthias R. Hohmann, Lisa Konieczny, Michelle Hackl, Brian Wirth, Talha Zaman, Raffi Enficiaud, Moritz Grosse-Wentrup, Bernhard Schölkopf:
MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware. UIST 2020: 1071-1084 - 2019
- [c282]Matthias R. Hohmann, Michelle Hackl, Brian Wirth, Talha Zaman, Raffi Enficiaud, Moritz Grosse-Wentrup, Bernhard Schölkopf:
MYND: A Platform for Large-scale Neuroscientific Studies. CHI Extended Abstracts 2019 - [c281]Atalanti-Anastasia Mastakouri, Bernhard Schölkopf, Moritz Grosse-Wentrup:
Beta Power May Meditate the Effect of Gamma-TACS on Motor Performance. EMBC 2019: 5902-5908 - [c280]Bernhard Schölkopf:
Learning causal mechanisms. GI-Jahrestagung 2019: 21 - [c279]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. RML@ICLR 2019 - [c278]Ðorðe Miladinovic, Muhammad Waleed Gondal, Bernhard Schölkopf, Joachim M. Buhmann, Stefan Bauer:
Disentangled State Space Models: Unsupervised Learning of dynamics across Heterogeneous Environments. DGS@ICLR 2019 - [c277]Gabriele Abbati, Philippe Wenk, Michael A. Osborne, Andreas Krause, Bernhard Schölkopf, Stefan Bauer:
AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs. ICML 2019: 1-10 - [c276]Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal, Amit Raj, James Hays, Bernhard Schölkopf:
Kernel Mean Matching for Content Addressability of GANs. ICML 2019: 3140-3151 - [c275]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. ICML 2019: 4114-4124 - [c274]Carl-Johann Simon-Gabriel, Yann Ollivier, Léon Bottou, Bernhard Schölkopf, David Lopez-Paz:
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension. ICML 2019: 5809-5817 - [c273]Raphael Suter, Ðorðe Miladinovic, Bernhard Schölkopf, Stefan Bauer:
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness. ICML 2019: 6056-6065 - [c272]Jiachen Xu, Vinay Jayaram, Bernhard Schölkopf, Moritz Grosse-Wentrup:
Feature extraction from the Hermitian manifold for Brain-Computer Interfaces. NER 2019: 965-968 - [c271]Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf, Wittawat Jitkrittum:
Kernel Stein Tests for Multiple Model Comparison. NeurIPS 2019: 2240-2250 - [c270]Kristof Meding, Dominik Janzing, Bernhard Schölkopf, Felix A. Wichmann:
Perceiving the arrow of time in autoregressive motion. NeurIPS 2019: 2303-2314 - [c269]Atalanti-Anastasia Mastakouri, Bernhard Schölkopf, Dominik Janzing:
Selecting causal brain features with a single conditional independence test per feature. NeurIPS 2019: 12532-12543 - [c268]Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem:
On the Fairness of Disentangled Representations. NeurIPS 2019: 14584-14597 - [c267]Muhammad Waleed Gondal, Manuel Wuthrich, Djordje Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer:
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. NeurIPS 2019: 15714-15725 - [c266]Ozan Özdenizci, Timm Meyer, Felix A. Wichmann, Jan Peters, Bernhard Schölkopf, Müjdat Çetin, Moritz Grosse-Wentrup:
Neural Signatures of Motor Skill in the Resting Brain. SMC 2019: 4387-4394 - [c265]Philipp Geiger, Michel Besserve, Justus Winkelmann, Claudius Proissl, Bernhard Schölkopf:
Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory. UAI 2019: 207-216 - [c264]Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, Bernhard Schölkopf:
The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA. UAI 2019: 217-227 - 2018
- [c263]Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing:
Group invariance principles for causal generative models. AISTATS 2018: 557-565 - [c262]Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf:
Cause-Effect Inference by Comparing Regression Errors. AISTATS 2018: 900-909 - [c261]Friedrich Solowjow, Arash Mehrjou, Bernhard Schölkopf, Sebastian Trimpe:
Efficient Encoding of Dynamical Systems Through Local Approximations. CDC 2018: 6073-6079 - [c260]Muhammad Waleed Gondal, Bernhard Schölkopf, Michael Hirsch:
The Unreasonable Effectiveness of Texture Transfer for Single Image Super-Resolution. ECCV Workshops (5) 2018: 80-97 - [c259]Tae Hyun Kim, Mehdi S. M. Sajjadi, Michael Hirsch, Bernhard Schölkopf:
Spatio-Temporal Transformer Network for Video Restoration. ECCV (3) 2018: 111-127 - [c258]Matthias Bauer, Valentin Volchkov, Michael Hirsch, Bernhard Schölkopf:
Automatic estimation of modulation transfer functions. ICCP 2018: 1-12 - [c257]Mostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schölkopf:
Fidelity-Weighted Learning. ICLR (Poster) 2018 - [c256]Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf:
Clustering Meets Implicit Generative Models. ICLR (Workshop) 2018 - [c255]Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin:
Learning Disentangled Representations with Wasserstein Auto-Encoders. ICLR (Workshop) 2018 - [c254]Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin:
Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders. ICLR (Workshop) 2018 - [c253]Mehdi S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou, Bernhard Schölkopf:
Tempered Adversarial Networks. ICLR (Workshop) 2018 - [c252]Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf:
Wasserstein Auto-Encoders. ICLR 2018 - [c251]Matej Balog, Ilya O. Tolstikhin, Bernhard Schölkopf:
Differentially Private Database Release via Kernel Mean Embeddings. ICML 2018: 423-431 - [c250]Dominik Janzing, Bernhard Schölkopf:
Detecting non-causal artifacts in multivariate linear regression models. ICML 2018: 2250-2258 - [c249]Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi:
On Matching Pursuit and Coordinate Descent. ICML 2018: 3204-3213 - [c248]Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, Bernhard Schölkopf:
Learning Independent Causal Mechanisms. ICML 2018: 4033-4041 - [c247]Mehdi S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou, Bernhard Schölkopf:
Tempered Adversarial Networks. ICML 2018: 4448-4456 - [c246]Biwei Huang, Kun Zhang, Yizhu Lin, Bernhard Schölkopf, Clark Glymour:
Generalized Score Functions for Causal Discovery. KDD 2018: 1551-1560 - [c245]Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton:
Informative Features for Model Comparison. NeurIPS 2018: 816-827 - [c244]Alexander Neitz, Giambattista Parascandolo, Stefan Bauer, Bernhard Schölkopf:
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models. NeurIPS 2018: 9838-9848 - [c243]Paul K. Rubenstein, Stephan Bongers, Joris M. Mooij, Bernhard Schölkopf:
From Deterministic ODEs to Dynamic Structural Causal Models. UAI 2018: 114-123 - [c242]Jooyeon Kim, Behzad Tabibian, Alice Oh, Bernhard Schölkopf, Manuel Gomez-Rodriguez:
Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation. WSDM 2018: 324-332 - 2017
- [c241]Anant Raj, Abhishek Kumar, Youssef Mroueh, Tom Fletcher, Bernhard Schölkopf:
Local Group Invariant Representations via Orbit Embeddings. AISTATS 2017: 1225-1235 - [c240]David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Schölkopf, Léon Bottou:
Discovering Causal Signals in Images. CVPR 2017: 58-66 - [c239]Chaochao Lu, Michael Hirsch, Bernhard Schölkopf:
Flexible Spatio-Temporal Networks for Video Prediction. CVPR 2017: 2137-2145 - [c238]Marius Goerner, Bernhard Schölkopf, Moritz Grosse-Wentrup:
Closing One's eyes Affects amplitude modulation but not frequency modulation in a Cognitive BCI. GBCIC 2017 - [c237]Julia Moser, Matthias Hohmann, Bernhard Schölkopf, Moritz Grosse-Wentrup:
A Guided Task for Cognitive brain-Computer Interfaces. GBCIC 2017 - [c236]Patrick Wieschollek, Michael Hirsch, Bernhard Schölkopf, Hendrik P. A. Lensch:
Learning Blind Motion Deblurring. ICCV 2017: 231-240 - [c235]Tae Hyun Kim, Kyoung Mu Lee, Bernhard Schölkopf, Michael Hirsch:
Online Video Deblurring via Dynamic Temporal Blending Network. ICCV 2017: 4058-4067 - [c234]Mehdi S. M. Sajjadi, Bernhard Schölkopf, Michael Hirsch:
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis. ICCV 2017: 4501-4510 - [c233]Biwei Huang, Kun Zhang, Jiji Zhang, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf:
Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows. ICDM 2017: 913-918 - [c232]Kun Zhang, Biwei Huang, Jiji Zhang, Clark Glymour, Bernhard Schölkopf:
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination. IJCAI 2017: 1347-1353 - [c231]Vinay Jayaram, Bernhard Schölkopf, Moritz Grosse-Wentrup:
Frequency peak features for low-channel classification in motor imagery paradigms. NER 2017: 321-324 - [c230]Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf:
Avoiding Discrimination through Causal Reasoning. NIPS 2017: 656-666 - [c229]Shixiang Gu, Tim Lillicrap, Richard E. Turner, Zoubin Ghahramani, Bernhard Schölkopf, Sergey Levine:
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. NIPS 2017: 3846-3855 - [c228]Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf:
AdaGAN: Boosting Generative Models. NIPS 2017: 5424-5433 - [c227]Anastasia-Atalanti Mastakouri, Sebastian Weichwald, Ozan Özdenizci, Timm Meyer, Bernhard Schölkopf, Moritz Grosse-Wentrup:
Personalized brain-computer interface models for motor rehabilitation. SMC 2017: 3024-3029 - [c226]Mingming Gong, Kun Zhang, Bernhard Schölkopf, Clark Glymour, Dacheng Tao:
Causal Discovery from Temporally Aggregated Time Series. UAI 2017 - [c225]Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf:
Causal Consistency of Structural Equation Models. UAI 2017 - [c224]Rohit Babbar, Bernhard Schölkopf:
DiSMEC: Distributed Sparse Machines for Extreme Multi-label Classification. WSDM 2017: 721-729 - [c223]Behzad Tabibian, Isabel Valera, Mehrdad Farajtabar, Le Song, Bernhard Schölkopf, Manuel Gomez-Rodriguez:
Distilling Information Reliability and Source Trustworthiness from Digital Traces. WWW 2017: 847-855 - 2016
- [c222]Patrick Wieschollek, Bernhard Schölkopf, Hendrik P. A. Lensch, Michael Hirsch:
End-to-End Learning for Image Burst Deblurring. ACCV (4) 2016: 35-51 - [c221]Mehdi S. M. Sajjadi, Rolf Köhler, Bernhard Schölkopf, Michael Hirsch:
Depth Estimation Through a Generative Model of Light Field Synthesis. GCPR 2016: 426-438 - [c220]Edgar D. Klenske, Philipp Hennig, Bernhard Schölkopf, Melanie Nicole Zeilinger:
Approximate dual control maintaining the value of information with an application to building control. ECC 2016: 800-806 - [c219]Sebastián Gómez-González, Gerhard Neumann, Bernhard Schölkopf, Jan Peters:
Using probabilistic movement primitives for striking movements. Humanoids 2016: 502-508 - [c218]Yanlong Huang, Dieter Buchler, Okan Koc, Bernhard Schölkopf, Jan Peters:
Jointly learning trajectory generation and hitting point prediction in robot table tennis. Humanoids 2016: 650-655 - [c217]Stefan Bauer, Bernhard Schölkopf, Jonas Peters:
The Arrow of Time in Multivariate Time Series. ICML 2016: 2043-2051 - [c216]Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf:
Domain Adaptation with Conditional Transferable Components. ICML 2016: 2839-2848 - [c215]Carl-Johann Simon-Gabriel, Adam Scibior, Ilya O. Tolstikhin, Bernhard Schölkopf:
Consistent Kernel Mean Estimation for Functions of Random Variables. NIPS 2016: 1732-1740 - [c214]Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Bernhard Schölkopf:
Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels. NIPS 2016: 1930-1938 - [c213]Sebastian Weichwald, Arthur Gretton, Bernhard Schölkopf, Moritz Grosse-Wentrup:
Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data. PRNI 2016: 1-4 - [c212]Rohit Babbar, Krikamol Muandet, Bernhard Schölkopf:
TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification. SDM 2016: 234-242 - [c211]Kun Zhang, Jiji Zhang, Biwei Huang, Bernhard Schölkopf, Clark Glymour:
On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection. UAI 2016 - [c210]David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, Vladimir Vapnik:
Unifying distillation and privileged information. ICLR (Poster) 2016 - 2015
- [c209]Kun Zhang, Mingming Gong, Bernhard Schölkopf:
Multi-Source Domain Adaptation: A Causal View. AAAI 2015: 3150-3157 - [c208]Eleni Sgouritsa, Dominik Janzing, Philipp Hennig, Bernhard Schölkopf:
Inference of Cause and Effect with Unsupervised Inverse Regression. AISTATS 2015 - [c207]Vinay Jayaram, Natalie Widmann, Christian Forster, Tatiana Fomina, Matthias Hohmann, Jennifer Muller vom Hagen, Matthis Synofzik, Bernhard Schölkopf, Ludger Schöls, Moritz Grosse-Wentrup:
Brain-computer interfacing in amyotrophic lateral sclerosis: Implications of a resting-state EEG analysis. EMBC 2015: 6979-6982 - [c206]Tatiana Fomina, Matthias Hohmann, Bernhard Schölkopf, Moritz Grosse-Wentrup:
Identification of the Default Mode Network with electroencephalography. EMBC 2015: 7566-7569 - [c205]Michael Hirsch, Bernhard Schölkopf:
Self-Calibration of Optical Lenses. ICCV 2015: 612-620 - [c204]Alexander Loktyushin, Christian J. Schuler, Klaus Scheffler, Bernhard Schölkopf:
Retrospective Motion Correction of Magnitude-Input MR Images. MLMMI@ICML 2015: 3-12 - [c203]Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf, Michel Besserve:
Telling cause from effect in deterministic linear dynamical systems. ICML 2015: 285-294 - [c202]David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Ilya O. Tolstikhin:
Towards a Learning Theory of Cause-Effect Inference. ICML 2015: 1452-1461 - [c201]Mingming Gong, Kun Zhang, Bernhard Schölkopf, Dacheng Tao, Philipp Geiger:
Discovering Temporal Causal Relations from Subsampled Data. ICML 2015: 1898-1906 - [c200]Philipp Geiger, Kun Zhang, Bernhard Schölkopf, Mingming Gong, Dominik Janzing:
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components. ICML 2015: 1917-1925 - [c199]Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters:
Removing systematic errors for exoplanet search via latent causes. ICML 2015: 2218-2226 - [c198]Biwei Huang, Kun Zhang, Bernhard Schölkopf:
Identification of Time-Dependent Causal Model: A Gaussian Process Treatment. IJCAI 2015: 3561-3568 - [c197]Yanlong Huang, Bernhard Schölkopf, Jan Peters:
Learning optimal striking points for a ping-pong playing robot. IROS 2015: 4587-4592 - [c196]Mohammad Khatami, Tobias Schmidt-Wilcke, Pia C. Sundgren, Amin Abbasloo, Bernhard Schölkopf, Thomas Schultz:
BundleMAP: Anatomically Localized Features from dMRI for Detection of Disease. MLMI 2015: 52-60 - [c195]Matthias R. Hohmann, Tatiana Fomina, Vinay Jayaram, Natalie Widmann, Christian Forster, Jennifer Muller vom Hagen, Matthis Synofzik, Bernhard Schölkopf, Ludger Schöls, Moritz Grosse-Wentrup:
A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis. SMC 2015: 3187-3191 - 2014
- [c194]Dustin Lang, David W. Hogg, Bernhard Schölkopf:
Towards building a Crowd-Sourced Sky Map. AISTATS 2014: 549-557 - [c193]Sebastian Weichwald, Timm Meyer, Bernhard Schölkopf, Tonio Ball, Moritz Grosse-Wentrup:
Decoding index finger position from EEG using random forests. CIP 2014: 1-6 - [c192]Manuel Gomez-Rodriguez, Le Song, Bernhard Schölkopf:
Open Problem: Finding Good Cascade Sampling Processes for the Network Inference Problem. COLT 2014: 1276-1279 - [c191]Lyndsey C. Pickup, Zheng Pan, Donglai Wei, Yi-Chang Shih, Changshui Zhang, Andrew Zisserman, Bernhard Schölkopf, William T. Freeman:
Seeing the Arrow of Time. CVPR 2014: 2043-2050 - [c190]Rolf Köhler, Christian J. Schuler, Bernhard Schölkopf, Stefan Harmeling:
Mask-Specific Inpainting with Deep Neural Networks. GCPR 2014: 523-534 - [c189]Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Estimation and Stein Effect. ICML 2014: 10-18 - [c188]Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schölkopf:
Consistency of Causal Inference under the Additive Noise Model. ICML 2014: 478-486 - [c187]Hadi Daneshmand, Manuel Gomez-Rodriguez, Le Song, Bernhard Schölkopf:
Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm. ICML 2014: 793-801 - [c186]David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf:
Randomized Nonlinear Component Analysis. ICML 2014: 1359-1367 - [c185]Manuel Gomez-Rodriguez, Krishna P. Gummadi, Bernhard Schölkopf:
Quantifying Information Overload in Social Media and Its Impact on Social Contagions. ICWSM 2014 - [c184]Krikamol Muandet, Bharath K. Sriperumbudur, Bernhard Schölkopf:
Kernel Mean Estimation via Spectral Filtering. NIPS 2014: 1-9 - [c183]Sebastian Weichwald, Bernhard Schölkopf, Tonio Ball, Moritz Grosse-Wentrup:
Causal and anti-causal learning in pattern recognition for neuroimaging. PRNI 2014: 1-4 - [c182]Rafael Chaves, Lukas Luft, Thiago O. Maciel, David Gross, Dominik Janzing, Bernhard Schölkopf:
Inferring latent structures via information inequalities. UAI 2014: 112-121 - [c181]Gary Doran, Krikamol Muandet, Kun Zhang, Bernhard Schölkopf:
A Permutation-Based Kernel Conditional Independence Test. UAI 2014: 132-141 - [c180]Philipp Geiger, Dominik Janzing, Bernhard Schölkopf:
Estimating Causal Effects by Bounding Confounding. UAI 2014: 240-249 - 2013
- [c179]Edgar D. Klenske, Melanie Nicole Zeilinger, Bernhard Schölkopf, Philipp Hennig:
Nonparametric dynamics estimation for time periodic systems. Allerton 2013: 486-493 - [c178]Yevgeny Seldin, Bernhard Schölkopf:
On the Relations and Differences Between Popper Dimension, Exclusion Dimension and VC-Dimension. Empirical Inference 2013: 53-57 - [c177]Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij:
Semi-supervised Learning in Causal and Anticausal Settings. Empirical Inference 2013: 129-141 - [c176]Christian J. Schuler, Harold Christopher Burger, Stefan Harmeling, Bernhard Schölkopf:
A Machine Learning Approach for Non-blind Image Deconvolution. CVPR 2013: 1067-1074 - [c175]Stefan Harmeling, Michael Hirsch, Bernhard Schölkopf:
On a Link Between Kernel Mean Maps and Fraunhofer Diffraction, with an Application to Super-Resolution Beyond the Diffraction Limit. CVPR 2013: 1083-1090 - [c174]Kun Zhang, Zhikun Wang, Bernhard Schölkopf:
On Estimation of Functional Causal Models: Post-Nonlinear Causal Model as an Example. ICDM Workshops 2013: 139-146 - [c173]Rolf Köhler, Michael Hirsch, Bernhard Schölkopf, Stefan Harmeling:
Improving alpha matting and motion blurred foreground estimation. ICIP 2013: 3446-3450 - [c172]Krikamol Muandet, David Balduzzi, Bernhard Schölkopf:
Domain Generalization via Invariant Feature Representation. ICML (1) 2013: 10-18 - [c171]Manuel Gomez-Rodriguez, Jure Leskovec, Bernhard Schölkopf:
Modeling Information Propagation with Survival Theory. ICML (3) 2013: 666-674 - [c170]Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, Zhikun Wang:
Domain Adaptation under Target and Conditional Shift. ICML (3) 2013: 819-827 - [c169]David López-Paz, Philipp Hennig, Bernhard Schölkopf:
The Randomized Dependence Coefficient. NIPS 2013: 1-9 - [c168]Jonas Peters, Dominik Janzing, Bernhard Schölkopf:
Causal Inference on Time Series using Restricted Structural Equation Models. NIPS 2013: 154-162 - [c167]Michel Besserve, Nikos K. Logothetis, Bernhard Schölkopf:
Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators. NIPS 2013: 2535-2543 - [c166]Moritz Grosse-Wentrup, Stefan Harmeling, Thorsten O. Zander, N. Jeremy Hill, Bernhard Schölkopf:
How to Test the Quality of Reconstructed Sources in Independent Component Analysis (ICA) of EEG/MEG Data. PRNI 2013: 102-105 - [c165]Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
From Ordinary Differential Equations to Structural Causal Models: the deterministic case. UAI 2013 - [c164]Krikamol Muandet, Bernhard Schölkopf:
One-Class Support Measure Machines for Group Anomaly Detection. UAI 2013 - [c163]Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schölkopf:
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. UAI 2013 - [c162]Manuel Gomez-Rodriguez, Jure Leskovec, Bernhard Schölkopf:
Structure and dynamics of information pathways in online media. WSDM 2013: 23-32 - 2012
- [c161]Rolf Köhler, Michael Hirsch, Betty J. Mohler, Bernhard Schölkopf, Stefan Harmeling:
Recording and Playback of Camera Shake: Benchmarking Blind Deconvolution with a Real-World Database. ECCV (7) 2012: 27-40 - [c160]Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf:
Blind Correction of Optical Aberrations. ECCV (3) 2012: 187-200 - [c159]Michael Hirsch, Matthias Hofmann, Frederic Mantlik, Bernd J. Pichler, Bernhard Schölkopf, Michael Habeck:
A blind deconvolution approach for pseudo CT prediction from MR image pairs. ICIP 2012: 2953-2956 - [c158]Manuel Gomez-Rodriguez, Bernhard Schölkopf:
Influence Maximization in Continuous Time Diffusion Networks. ICML 2012 - [c157]Manuel Gomez-Rodriguez, Bernhard Schölkopf:
Submodular Inference of Diffusion Networks from Multiple Trees. ICML 2012 - [c156]Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij:
On causal and anticausal learning. ICML 2012 - [c155]Timm Meyer, Jan Peters, Doris Brtz, Thorsten O. Zander, Bernhard Schölkopf, Surjo R. Soekadar, Moritz Grosse-Wentrup:
A brain-robot interface for studying motor learning after stroke. IROS 2012: 4078-4083 - [c154]Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo, Bernhard Schölkopf:
Learning from Distributions via Support Measure Machines. NIPS 2012: 10-18 - [c153]Francesco Dinuzzo, Bernhard Schölkopf:
The representer theorem for Hilbert spaces: a necessary and sufficient condition. NIPS 2012: 189-196 - [c152]David López-Paz, José Miguel Hernández-Lobato, Bernhard Schölkopf:
Semi-Supervised Domain Adaptation with Non-Parametric Copulas. NIPS 2012: 674-682 - [c151]Zhikun Wang, Marc Peter Deisenroth, Heni Ben Amor, David Vogt, Bernhard Schölkopf, Jan Peters:
Probabilistic Modeling of Human Movements for Intention Inference. Robotics: Science and Systems 2012 - 2011
- [c150]Michel Besserve, Dominik Janzing, Nikos K. Logothetis, Bernhard Schölkopf:
Finding dependencies between frequencies with the kernel cross-spectral density. ICASSP 2011: 2080-2083 - [c149]Harold Christopher Burger, Bernhard Schölkopf, Stefan Harmeling:
Removing noise from astronomical images using a pixel-specific noise model. ICCP 2011: 1-8 - [c148]Michael Hirsch, Christian J. Schuler, Stefan Harmeling, Bernhard Schölkopf:
Fast removal of non-uniform camera shake. ICCV 2011: 463-470 - [c147]Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf:
Non-stationary correction of optical aberrations. ICCV 2011: 659-666 - [c146]Manuel Gomez-Rodriguez, David Balduzzi, Bernhard Schölkopf:
Uncovering the Temporal Dynamics of Diffusion Networks. ICML 2011: 561-568 - [c145]Vojtech Franc, Alexander Zien, Bernhard Schölkopf:
Support Vector Machines as Probabilistic Models. ICML 2011: 665-672 - [c144]Zhikun Wang, Christoph H. Lampert, Katharina Mülling, Bernhard Schölkopf, Jan Peters:
Learning anticipation policies for robot table tennis. IROS 2011: 332-337 - [c143]Botond Bocsi, Duy Nguyen-Tuong, Lehel Csató, Bernhard Schölkopf, Jan Peters:
Learning inverse kinematics with structured prediction. IROS 2011: 698-703 - [c142]Panagiotis Achlioptas, Bernhard Schölkopf, Karsten M. Borgwardt:
Two-locus association mapping in subquadratic time. KDD 2011: 726-734 - [c141]Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf:
On Causal Discovery with Cyclic Additive Noise Models. NIPS 2011: 639-647 - [c140]Peter V. Gehler, Carsten Rother, Martin Kiefel, Lumin Zhang, Bernhard Schölkopf:
Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance. NIPS 2011: 765-773 - [c139]Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schölkopf:
Detecting low-complexity unobserved causes. UAI 2011: 383-391 - [c138]Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
Identifiability of Causal Graphs using Functional Models. UAI 2011: 589-598 - [c137]Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf:
Kernel-based Conditional Independence Test and Application in Causal Discovery. UAI 2011: 804-813 - 2010
- [c136]Bastian Steudel, Dominik Janzing, Bernhard Schölkopf:
Causal Markov Condition for Submodular Information Measures. COLT 2010: 464-476 - [c135]Michael Hirsch, Suvrit Sra, Bernhard Schölkopf, Stefan Harmeling:
Efficient filter flow for space-variant multiframe blind deconvolution. CVPR 2010: 607-614 - [c134]Stefan Harmeling, Suvrit Sra, Michael Hirsch, Bernhard Schölkopf:
Multiframe blind deconvolution, super-resolution, and saturation correction via incremental EM. ICIP 2010: 3313-3316 - [c133]Dominik Janzing, Patrik O. Hoyer, Bernhard Schölkopf:
Telling cause from effect based on high-dimensional observations. ICML 2010: 479-486 - [c132]Jens Kober, Katharina Mülling, Oliver Kroemer, Christoph H. Lampert, Bernhard Schölkopf, Jan Peters:
Movement templates for learning of hitting and batting. ICRA 2010: 853-858 - [c131]Valentin Schwamberger, Pham Hai Dang Le, Bernhard Schölkopf, Matthias O. Franz:
The Influence of the Image Basis on Modeling and Steganalysis Performance. Information Hiding 2010: 133-144 - [c130]Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf, Gert R. G. Lanckriet:
Non-parametric estimation of integral probability metrics. ISIT 2010: 1428-1432 - [c129]Mauricio A. Álvarez, Jan Peters, Bernhard Schölkopf, Neil D. Lawrence:
Switched Latent Force Models for Movement Segmentation. NIPS 2010: 55-63 - [c128]Stefan Harmeling, Michael Hirsch, Bernhard Schölkopf:
Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake. NIPS 2010: 829-837 - [c127]Joris M. Mooij, Oliver Stegle, Dominik Janzing, Kun Zhang, Bernhard Schölkopf:
Probabilistic latent variable models for distinguishing between cause and effect. NIPS 2010: 1687-1695 - [c126]Michael Hirsch, Bernhard Schölkopf, Michael Habeck:
A New Algorithm for Improving the Resolution of Cryo-EM Density Maps. RECOMB 2010: 174-188 - [c125]Manuel Gomez-Rodriguez, Jan Peters, N. Jeremy Hill, Bernhard Schölkopf, Alireza Gharabaghi, Moritz Grosse-Wentrup:
Closing the sensorimotor loop: Haptic feedback facilitates decoding of arm movement imagery. SMC 2010: 121-126 - [c124]Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf:
Inferring deterministic causal relations. UAI 2010: 143-150 - [c123]Kun Zhang, Bernhard Schölkopf, Dominik Janzing:
Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery. UAI 2010: 717-724 - [c122]Isabelle Guyon, Dominik Janzing, Bernhard Schölkopf:
Causality: Objectives and Assessment. NIPS Causality: Objectives and Assessment 2010: 1-42 - [c121]Jonas Peters, Dominik Janzing, Bernhard Schölkopf:
Identifying Cause and Effect on Discrete Data using Additive Noise Models. AISTATS 2010: 597-604 - 2009
- [c120]Daewon Lee, Matthias Hofmann, Florian Steinke, Yasemin Altun, Nathan D. Cahill, Bernhard Schölkopf:
Learning similarity measure for multi-modal 3D image registration. CVPR 2009: 186-193 - [c119]Christian Walder, Martin Breidt, Heinrich H. Bülthoff, Bernhard Schölkopf, Cristóbal Curio:
Markerless 3D Face Tracking. DAGM-Symposium 2009: 41-50 - [c118]Joris M. Mooij, Dominik Janzing, Jonas Peters, Bernhard Schölkopf:
Regression by dependence minimization and its application to causal inference in additive noise models. ICML 2009: 745-752 - [c117]Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf:
Detecting the direction of causal time series. ICML 2009: 801-808 - [c116]Duy Nguyen-Tuong, Bernhard Schölkopf, Jan Peters:
Sparse online model learning for robot control with support vector regression. IROS 2009: 3121-3126 - [c115]Elisabeth Georgii, Koji Tsuda, Bernhard Schölkopf:
Multi-way set enumeration in real-valued tensors. KDD Workshop on Data Mining using Matrices and Tensors 2009 - [c114]Stefanie Jegelka, Arthur Gretton, Bernhard Schölkopf, Bharath K. Sriperumbudur, Ulrike von Luxburg:
Generalized Clustering via Kernel Embeddings. KI 2009: 144-152 - [c113]Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Gert R. G. Lanckriet, Bernhard Schölkopf:
Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions. NIPS 2009: 1750-1758 - [c112]Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf:
Identifying confounders using additive noise models. UAI 2009: 249-257 - 2008
- [c111]Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Gert R. G. Lanckriet, Bernhard Schölkopf:
Injective Hilbert Space Embeddings of Probability Measures. COLT 2008: 111-122 - [c110]Guillaume Charpiat, Matthias Hofmann, Bernhard Schölkopf:
Automatic Image Colorization Via Multimodal Predictions. ECCV (3) 2008: 126-139 - [c109]Duy Nguyen-Tuong, Jan Peters, Matthias W. Seeger, Bernhard Schölkopf:
Learning Inverse Dynamics: a Comparison. ESANN 2008: 13-18 - [c108]Pia Breuer, Kwang In Kim, Wolf Kienzle, Bernhard Schölkopf, Volker Blanz:
Automatic 3D face reconstruction from single images or video. FG 2008: 1-8 - [c107]Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf:
Kernel Methods for Detecting the Direction of Time Series. GfKl 2008: 57-66 - [c106]Le Song, Xinhua Zhang, Alexander J. Smola, Arthur Gretton, Bernhard Schölkopf:
Tailoring density estimation via reproducing kernel moment matching. ICML 2008: 992-999 - [c105]Christian Walder, Kwang In Kim, Bernhard Schölkopf:
Sparse multiscale gaussian process regression. ICML 2008: 1112-1119 - [c104]Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf:
Characteristic Kernels on Groups and Semigroups. NIPS 2008: 473-480 - [c103]N. Jeremy Hill, Jason Farquhar, Suzanna Martens, Felix Bießmann, Bernhard Schölkopf:
Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance. NIPS 2008: 665-672 - [c102]Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, Bernhard Schölkopf:
Nonlinear causal discovery with additive noise models. NIPS 2008: 689-696 - [c101]Gabriele Beate Schweikert, Christian Widmer, Bernhard Schölkopf, Gunnar Rätsch:
An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis. NIPS 2008: 1433-1440 - [c100]Matthias W. Seeger, Hannes Nickisch, Rolf Pohmann, Bernhard Schölkopf:
Bayesian Experimental Design of Magnetic Resonance Imaging Sequences. NIPS 2008: 1441-1448 - [c99]Christian Walder, Bernhard Schölkopf:
Diffeomorphic Dimensionality Reduction. NIPS 2008: 1713-1720 - 2007
- [c98]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Approach to Comparing Distributions. AAAI 2007: 1637-1641 - [c97]Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf:
A Hilbert Space Embedding for Distributions. ALT 2007: 13-31 - [c96]Jan Peters, Stefan Schaal, Bernhard Schölkopf:
Towards Machine Learning of Motor Skills. AMS 2007: 138-144 - [c95]Wolf Kienzle, Bernhard Schölkopf, Felix A. Wichmann, Matthias O. Franz:
How to Find Interesting Locations in Video: A Spatiotemporal Interest Point Detector Learned from Human Eye Movements. DAGM-Symposium 2007: 405-414 - [c94]Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf:
A Hilbert Space Embedding for Distributions. Discovery Science 2007: 40-41 - [c93]Xiaohai Sun, Dominik Janzing, Bernhard Schölkopf:
Distinguishing between cause and effect via kernel-based complexity measures for conditional distributions. ESANN 2007: 441-446 - [c92]Xiaohai Sun, Dominik Janzing, Bernhard Schölkopf, Kenji Fukumizu:
A kernel-based causal learning algorithm. ICML 2007: 855-862 - [c91]Mingrui Wu, Kai Yu, Shipeng Yu, Bernhard Schölkopf:
Local learning projections. ICML 2007: 1039-1046 - [c90]Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, Bernhard Schölkopf:
Kernel Measures of Conditional Dependence. NIPS 2007: 489-496 - [c89]Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le Song, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Statistical Test of Independence. NIPS 2007: 585-592 - [c88]Fabian H. Sinz, Olivier Chapelle, Alekh Agarwal, Bernhard Schölkopf:
An Analysis of Inference with the Universum. NIPS 2007: 1369-1376 - [c87]Mingrui Wu, Bernhard Schölkopf:
Transductive Classification via Local Learning Regularization. AISTATS 2007: 628-635 - 2006
- [c86]Wolf Kienzle, Felix A. Wichmann, Bernhard Schölkopf, Matthias O. Franz:
Learning an Interest Operator from Human Eye Movements. CVPR Workshops 2006: 24 - [c85]N. Jeremy Hill, Thomas Navin Lal, Michael Schröder, Thilo Hinterberger, Guido Widman, Christian Erich Elger, Bernhard Schölkopf, Niels Birbaumer:
Classifying Event-Related Desynchronization in EEG, ECoG and MEG Signals. DAGM-Symposium 2006: 404-413 - [c84]Xiaohai Sun, Dominik Janzing, Bernhard Schölkopf:
Causal Inference by Choosing Graphs with Most Plausible Markov Kernels. AI&M 2006 - [c83]Karsten M. Borgwardt, Arthur Gretton, Malte J. Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, Alexander J. Smola:
Integrating structured biological data by Kernel Maximum Mean Discrepancy. ISMB (Supplement of Bioinformatics) 2006: 49-57 - [c82]Christian Walder, Bernhard Schölkopf, Olivier Chapelle:
Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions. NIPS 2006: 273-280 - [c81]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Method for the Two-Sample-Problem. NIPS 2006: 513-520 - [c80]Jiayuan Huang, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Bernhard Schölkopf:
Correcting Sample Selection Bias by Unlabeled Data. NIPS 2006: 601-608 - [c79]Wolf Kienzle, Felix A. Wichmann, Bernhard Schölkopf, Matthias O. Franz:
A Nonparametric Approach to Bottom-Up Visual Saliency. NIPS 2006: 689-696 - [c78]Florian Steinke, Bernhard Schölkopf, Volker Blanz:
Learning Dense 3D Correspondence. NIPS 2006: 1313-1320 - [c77]Mingrui Wu, Bernhard Schölkopf:
A Local Learning Approach for Clustering. NIPS 2006: 1529-1536 - [c76]Dengyong Zhou, Jiayuan Huang, Bernhard Schölkopf:
Learning with Hypergraphs: Clustering, Classification, and Embedding. NIPS 2006: 1601-1608 - 2005
- [c75]Arthur Gretton, Alexander J. Smola, Olivier Bousquet, Ralf Herbrich, Andrei Belitski, Mark Augath, Yusuke Murayama, Jon Pauls, Bernhard Schölkopf, Nikos K. Logothetis:
Kernel Constrained Covariance for Dependence Measurement. AISTATS 2005: 112-119 - [c74]Arthur Gretton, Olivier Bousquet, Alexander J. Smola, Bernhard Schölkopf:
Measuring Statistical Dependence with Hilbert-Schmidt Norms. ALT 2005: 63-77 - [c73]Dengyong Zhou, Bernhard Schölkopf:
Regularization on Discrete Spaces. DAGM-Symposium 2005: 361-368 - [c72]Koji Tsuda, Hyunjung Shin, Bernhard Schölkopf:
Fast protein classification with multiple networks. ECCB/JBI 2005: 65 - [c71]Wolf Kienzle, Bernhard Schölkopf:
Training Support Vector Machines with Multiple Equality Constraints. ECML 2005: 182-193 - [c70]Thomas Navin Lal, Michael Schröder, N. Jeremy Hill, Hubert Preißl, Thilo Hinterberger, Jürgen Mellinger, Martin Bogdan, Wolfgang Rosenstiel, Thomas Hofmann, Niels Birbaumer, Bernhard Schölkopf:
A brain computer interface with online feedback based on magnetoencephalography. ICML 2005: 465-472 - [c69]Bernhard Schölkopf, Florian Steinke, Volker Blanz:
Object correspondence as a machine learning problem. ICML 2005: 776-783 - [c68]Sören Sonnenburg, Gunnar Rätsch, Bernhard Schölkopf:
Large scale genomic sequence SVM classifiers. ICML 2005: 848-855 - [c67]Christian Walder, Olivier Chapelle, Bernhard Schölkopf:
Implicit surface modelling as an eigenvalue problem. ICML 2005: 936-939 - [c66]Mingrui Wu, Bernhard Schölkopf, Gökhan H. Bakir:
Building Sparse Large Margin Classifiers. ICML 2005: 996-1003 - [c65]Dengyong Zhou, Jiayuan Huang, Bernhard Schölkopf:
Learning from labeled and unlabeled data on a directed graph. ICML 2005: 1036-1043 - [c64]Gunnar Rätsch, Sören Sonnenburg, Bernhard Schölkopf:
RASE: recognition of alternatively spliced exons in C.elegans. ISMB (Supplement of Bioinformatics) 2005: 369-377 - [c63]Jason Weston, Bernhard Schölkopf, Olivier Bousquet:
Joint Kernel Maps. IWANN 2005: 176-191 - [c62]Tobias Jung, Luis Javier Herrera, Bernhard Schölkopf:
Long Term Prediction of Product Quality in a Glass Manufacturing Process Using a Kernel Based Approach. IWANN 2005: 960-967 - [c61]Joaquin Quiñonero Candela, Carl Edward Rasmussen, Fabian H. Sinz, Olivier Bousquet, Bernhard Schölkopf:
Evaluating Predictive Uncertainty Challenge. MLCW 2005: 1-27 - 2004
- [c60]Matthias O. Franz, Younghee Kwon, Carl Edward Rasmussen, Bernhard Schölkopf:
Semi-supervised Kernel Regression Using Whitened Function Classes. DAGM-Symposium 2004: 18-26 - [c59]Wolf Kienzle, Gökhan H. Bakir, Matthias O. Franz, Bernhard Schölkopf:
Efficient Approximations for Support Vector Machines in Object Detection. DAGM-Symposium 2004: 54-61 - [c58]Dengyong Zhou, Bernhard Schölkopf:
Learning from Labeled and Unlabeled Data Using Random Walks. DAGM-Symposium 2004: 237-244 - [c57]Gökhan H. Bakir, Arthur Gretton, Matthias O. Franz, Bernhard Schölkopf:
Multivariate Regression via Stiefel Manifold Constraints. DAGM-Symposium 2004: 262-269 - [c56]Jihun Ham, Daniel D. Lee, Sebastian Mika, Bernhard Schölkopf:
A kernel view of the dimensionality reduction of manifolds. ICML 2004 - [c55]Matthias O. Franz, Bernhard Schölkopf:
Implicit Wiener Series for Higher-Order Image Analysis. NIPS 2004: 465-472 - [c54]N. Jeremy Hill, Thomas Navin Lal, Karin Bierig, Niels Birbaumer, Bernhard Schölkopf:
An Auditory Paradigm for Brain-Computer Interfaces. NIPS 2004: 569-576 - [c53]Wolf Kienzle, Gökhan H. Bakir, Matthias O. Franz, Bernhard Schölkopf:
Face Detection - Efficient and Rank Deficient. NIPS 2004: 673-680 - [c52]Thomas Navin Lal, Thilo Hinterberger, Guido Widman, Michael Schröder, N. Jeremy Hill, Wolfgang Rosenstiel, Christian Erich Elger, Bernhard Schölkopf, Niels Birbaumer:
Methods Towards Invasive Human Brain Computer Interfaces. NIPS 2004: 737-744 - [c51]Bernhard Schölkopf, Joachim Giesen, Simon Spalinger:
Kernel Methods for Implicit Surface Modeling. NIPS 2004: 1193-1200 - [c50]Felix A. Wichmann, Arnulf B. A. Graf, Eero P. Simoncelli, Heinrich H. Bülthoff, Bernhard Schölkopf:
Machine Learning Applied to Perception: Decision Images for Gender Classification. NIPS 2004: 1489-1496 - [c49]Dengyong Zhou, Bernhard Schölkopf, Thomas Hofmann:
Semi-supervised Learning on Directed Graphs. NIPS 2004: 1633-1640 - 2003
- [c48]Holger Fröhlich, Olivier Chapelle, Bernhard Schölkopf:
Feature Selection for Support Vector Machines by Means of Genetic Algorithms. ICTAI 2003: 142-148 - [c47]Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, Bernhard Schölkopf:
Ranking on Data Manifolds. NIPS 2003: 169-176 - [c46]Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schölkopf:
Learning with Local and Global Consistency. NIPS 2003: 321-328 - [c45]Gökhan H. Bakir, Jason Weston, Bernhard Schölkopf:
Learning to Find Pre-Images. NIPS 2003: 449-456 - [c44]Jan Eichhorn, Andreas S. Tolias, Alexander Zien, Malte Kuss, Carl Edward Rasmussen, Jason Weston, Nikos K. Logothetis, Bernhard Schölkopf:
Prediction on Spike Data Using Kernel Algorithms. NIPS 2003: 1367-1374 - 2002
- [c43]Bernhard Schölkopf, Jason Weston, Eleazar Eskin, Christina S. Leslie, William Stafford Noble:
A Kernel Approach for Learning from almost Orthogonal Patterns. ECML 2002: 511-528 - [c42]Bernhard Schölkopf, Alexander J. Smola:
A Short Introduction to Learning with Kernels. Machine Learning Summer School 2002: 41-64 - [c41]Alexander J. Smola, Bernhard Schölkopf:
Bayesian Kernel Methods. Machine Learning Summer School 2002: 65-117 - [c40]Olivier Chapelle, Jason Weston, Bernhard Schölkopf:
Cluster Kernels for Semi-Supervised Learning. NIPS 2002: 585-592 - [c39]Jason Weston, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf, Vladimir Vapnik:
Kernel Dependency Estimation. NIPS 2002: 873-880 - [c38]Bernhard Schölkopf, Jason Weston, Eleazar Eskin, Christina S. Leslie, William Stafford Noble:
A Kernel Approach for Learning from Almost Orthogonal Patterns. PKDD 2002: 494-511 - 2001
- [c37]Sebastian Mika, Alexander J. Smola, Bernhard Schölkopf:
An improved training algorithm for kernel Fisher discriminants. AISTATS 2001: 209-215 - [c36]Michael E. Tipping, Bernhard Schölkopf:
A Kernel Approach for Vector Quantization with Guaranteed Distortion Bounds. AISTATS 2001: 298-303 - [c35]Bernhard Schölkopf, Ralf Herbrich, Alexander J. Smola:
A Generalized Representer Theorem. COLT/EuroCOLT 2001: 416-426 - [c34]Stan Z. Li, QingDong Fu, Lie Gu, Bernhard Schölkopf, Yimin Cheng, HongJiang Zhang:
Kernel Machine Based Learning for Multi-View Face Detection and Pose Estimation. ICCV 2001: 674-679 - [c33]Sami Romdhani, Philip H. S. Torr, Bernhard Schölkopf, Andrew Blake:
Computationally Efficient Face Detection. ICCV 2001: 695-700 - [c32]Neil D. Lawrence, Bernhard Schölkopf:
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise. ICML 2001: 306-313 - [c31]Dimitris Achlioptas, Frank McSherry, Bernhard Schölkopf:
Sampling Techniques for Kernel Methods. NIPS 2001: 335-342 - [c30]Olivier Chapelle, Bernhard Schölkopf:
Incorporating Invariances in Non-Linear Support Vector Machines. NIPS 2001: 609-616 - 2000
- [c29]Robert C. Williamson, Alexander J. Smola, Bernhard Schölkopf:
Entropy Numbers of Linear Function Classes. COLT 2000: 309-319 - [c28]Alexander J. Smola, Bernhard Schölkopf:
Sparse Greedy Matrix Approximation for Machine Learning. ICML 2000: 911-918 - [c27]Athanassia Chalimourda, Bernhard Schölkopf, Alexander J. Smola:
Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments. IJCNN (5) 2000: 199-204 - [c26]Bernhard Schölkopf:
The Kernel Trick for Distances. NIPS 2000: 301-307 - [c25]Susanne Still, Bernhard Schölkopf, Klaus Hepp, Rodney J. Douglas:
Four-legged Walking Gait Control Using a Neuromorphic Chip Interfaced to a Support Vector Learning Algorithm. NIPS 2000: 741-747 - [c24]Paul M. Hayton, Bernhard Schölkopf, Lionel Tarassenko, Paul Anuzis:
Support Vector Novelty Detection Applied to Jet Engine Vibration Spectra. NIPS 2000: 946-952 - [c23]Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Sebastian Mika, Takashi Onoda, Klaus-Robert Müller:
Robust Ensemble Learning for Data Mining. PAKDD 2000: 341-344 - 1999
- [c22]Alexander J. Smola, Robert C. Williamson, Sebastian Mika, Bernhard Schölkopf:
Regularized Principal Manifolds. EuroCOLT 1999: 214-229 - [c21]Robert C. Williamson, Alexander J. Smola, Bernhard Schölkopf:
Entropy Numbers, Operators and Support Vector Kernels. EuroCOLT 1999: 285-299 - [c20]Alexander Zien, Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Christian Lemmen, Alexander J. Smola, Thomas Lengauer, Klaus-Robert Müller:
Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites. German Conference on Bioinformatics 1999: 37-43 - [c19]Alexander J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson:
The Entropy Regularization Information Criterion. NIPS 1999: 342-348 - [c18]Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller:
Invariant Feature Extraction and Classification in Kernel Spaces. NIPS 1999: 526-532 - [c17]Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller, Takashi Onoda, Sebastian Mika:
v-Arc: Ensemble Learning in the Presence of Outliers. NIPS 1999: 561-567 - [c16]Bernhard Schölkopf, Robert C. Williamson, Alexander J. Smola, John Shawe-Taylor, John C. Platt:
Support Vector Method for Novelty Detection. NIPS 1999: 582-588 - 1998
- [c15]Bernhard Schölkopf, Alexander J. Smola, Phil Knirsch, Chris Burges:
Fast Approximation of Support Vector Kernel Expansions, and an Interpretation of Clustering as Approximation in Feature Spaces. DAGM-Symposium 1998: 125-132 - [c14]Matthias O. Franz, Bernhard Schölkopf, Hanspeter A. Mallot, Heinrich H. Bülthoff, Andreas Zell:
Navigation mit Schnappschüssen. DAGM-Symposium 1998: 421-428 - [c13]Bernhard Schölkopf, Peter L. Bartlett, Alexander J. Smola, Robert C. Williamson:
Shrinking the Tube: A New Support Vector Regression Algorithm. NIPS 1998: 330-336 - [c12]Sebastian Mika, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller, Matthias Scholz, Gunnar Rätsch:
Kernel PCA and De-Noising in Feature Spaces. NIPS 1998: 536-542 - [c11]Alexander J. Smola, Thilo-Thomas Frieß, Bernhard Schölkopf:
Semiparametric Support Vector and Linear Programming Machines. NIPS 1998: 585-591 - 1997
- [c10]Matthias O. Franz, Bernhard Schölkopf, Philip Georg, Hanspeter A. Mallot, Heinrich H. Bülthoff:
Learning View Graphs for Robot Navigation. Agents 1997: 138-147 - [c9]Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller:
Kernel Principal Component Analysis. ICANN 1997: 583-588 - [c8]Hanspeter A. Mallot, Matthias O. Franz, Bernhard Schölkopf, Heinrich H. Bülthoff:
The View-Graph Approach to Visual Navigation and Spatial Memory. ICANN 1997: 751-756 - [c7]Klaus-Robert Müller, Alexander J. Smola, Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, Vladimir Vapnik:
Predicting Time Series with Support Vector Machines. ICANN 1997: 999-1004 - [c6]Alexander J. Smola, Bernhard Schölkopf:
From Regularization Operators to Support Vector Kernels. NIPS 1997: 343-349 - [c5]Bernhard Schölkopf, Patrice Y. Simard, Alexander J. Smola, Vladimir Vapnik:
Prior Knowledge in Support Vector Kernels. NIPS 1997: 640-646 - 1996
- [c4]Bernhard Schölkopf, Chris Burges, Vladimir Vapnik:
Incorporating Invariances in Support Vector Learning Machines. ICANN 1996: 47-52 - [c3]Volker Blanz, Bernhard Schölkopf, Heinrich H. Bülthoff, Chris Burges, Vladimir Vapnik, Thomas Vetter:
Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models. ICANN 1996: 251-256 - [c2]Christopher J. C. Burges, Bernhard Schölkopf:
Improving the Accuracy and Speed of Support Vector Machines. NIPS 1996: 375-381 - 1995
- [c1]Bernhard Schölkopf, Chris Burges, Vladimir Vapnik:
Extracting Support Data for a Given Task. KDD 1995: 252-257
Parts in Books or Collections
- 2022
- [p7]Bernhard Schölkopf:
Causality for Machine Learning. Probabilistic and Causal Inference 2022: 765-804 - 2006
- [p6]Olivier Chapelle, Bernhard Schölkopf, Alexander Zien:
Introduction to Semi-Supervised Learning. Semi-Supervised Learning 2006: 1-12 - [p5]Dengyong Zhou, Bernhard Schölkopf:
Discrete Regularization. Semi-Supervised Learning 2006: 236-249 - [p4]Olivier Chapelle, Bernhard Schölkopf, Alexander Zien:
Analysis of Benchmarks. Semi-Supervised Learning 2006: 376-393 - [p3]Olivier Chapelle, Bernhard Schölkopf, Alexander Zien:
A Discussion of Semi-Supervised Learning and Transduction. Semi-Supervised Learning 2006: 473-478 - [p2]Thomas Navin Lal, Olivier Chapelle, Bernhard Schölkopf:
Combining a Filter Method with SVMs. Feature Extraction 2006: 439-445 - 2001
- [p1]Bernhard Schölkopf:
Statistical Learning and Kernel Methods. Data Fusion and Perception 2001: 3-24
Editorship
- 2022
- [e10]Bernhard Schölkopf, Caroline Uhler, Kun Zhang:
1st Conference on Causal Learning and Reasoning, CLeaR 2022, Sequoia Conference Center, Eureka, CA, USA, 11-13 April, 2022. Proceedings of Machine Learning Research 177, PMLR 2022 [contents] - 2013
- [e9]Bernhard Schölkopf, Zhiyuan Luo, Vladimir Vovk:
Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik. Springer 2013, ISBN 978-3-642-41135-9 [contents] - 2011
- [e8]Henry Horng-Shing Lu, Bernhard Schölkopf, Hongyu Zhao:
Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics, Springer 2011, ISBN 978-3-642-16344-9 [contents] - 2010
- [e7]Isabelle Guyon, Dominik Janzing, Bernhard Schölkopf:
Causality: Objectives and Assessment (NIPS 2008 Workshop), Whistler, Canada, December 12, 2008. JMLR Proceedings 6, JMLR.org 2010 [contents] - 2009
- [e6]Dominik Janzing, Steffen L. Lauritzen, Bernhard Schölkopf:
Machine learning approaches to statistical dependences and causality, 27.09. - 02.10.2009. Dagstuhl Seminar Proceedings 09401, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Germany 2009 [contents] - 2007
- [e5]Bernhard Schölkopf, John C. Platt, Thomas Hofmann:
Advances in Neural Information Processing Systems 19, Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 4-7, 2006. MIT Press 2007, ISBN 0-262-19568-2 [contents] - 2006
- [e4]Olivier Chapelle, Bernhard Schölkopf, Alexander Zien:
Semi-Supervised Learning. The MIT Press 2006, ISBN 9780262033589 [contents] - 2004
- [e3]Carl Edward Rasmussen, Heinrich H. Bülthoff, Bernhard Schölkopf, Martin A. Giese:
Pattern Recognition, 26th DAGM Symposium, August 30 - September 1, 2004, Tübingen, Germany, Proceedings. Lecture Notes in Computer Science 3175, Springer 2004, ISBN 3-540-22945-0 [contents] - [e2]Sebastian Thrun, Lawrence K. Saul, Bernhard Schölkopf:
Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, NIPS 2003, December 8-13, 2003, Vancouver and Whistler, British Columbia, Canada]. MIT Press 2004, ISBN 0-262-20152-6 [contents] - 2003
- [e1]Bernhard Schölkopf, Manfred K. Warmuth:
Computational Learning Theory and Kernel Machines, 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003, Proceedings. Lecture Notes in Computer Science 2777, Springer 2003, ISBN 3-540-40720-0 [contents]
Reference Works
- 2011
- [r1]Ulrike von Luxburg, Bernhard Schölkopf:
Statistical Learning Theory: Models, Concepts, and Results. Inductive Logic 2011: 651-706
Data and Artifacts
- 2023
- [d8]Patrik Reizinger, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel:
nl-causal-representations. Version v1.0.1. Zenodo, 2023 [all versions]
- [d7]Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato:
normflows: A PyTorch Package for Normalizing Flows. Version v1.7.0. Zenodo, 2023 [all versions] - [d6]Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato:
normflows: A PyTorch Package for Normalizing Flows. Version v1.7.1. Zenodo, 2023 [all versions] - [d5]Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato:
normflows: A PyTorch Package for Normalizing Flows. Version v1.7.2. Zenodo, 2023 [all versions] - [d4]Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato:
normflows: A PyTorch Package for Normalizing Flows. Version v1.7.3. Zenodo, 2023 [all versions] - 2022
- [d3]Julius von Kügelgen, Luigi Gresele, Bernhard Schölkopf:
Age-stratified Covid-19 case fatality rates (CFRs): different countries and longitudinal. IEEE DataPort, 2022 - [d2]Patrik Reizinger, Luigi Gresele, Jack Brady, Dominik Zietlow, Julius von Kügelgen, Michel Besserve, Georg Martius, Wieland Brendel, Bernhard Schölkopf:
ima-vae. Zenodo, 2022 - [d1]Patrik Reizinger, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel:
nl-causal-representations. Version 1.0.0. Zenodo, 2022 [all versions]
Informal and Other Publications
- 2024
- [i356]Partha Ghosh, Soubhik Sanyal, Cordelia Schmid, Bernhard Schölkopf:
RAVEN: Rethinking Adversarial Video Generation with Efficient Tri-plane Networks. CoRR abs/2401.06035 (2024) - [i355]Jan Schneider, Pierre Schumacher, Simon Guist, Le Chen, Daniel F. B. Häufle, Bernhard Schölkopf, Dieter Büchler:
Identifying Policy Gradient Subspaces. CoRR abs/2401.06604 (2024) - [i354]Andreas Opedal, Alessandro Stolfo, Haruki Shirakami, Ying Jiao, Ryan Cotterell, Bernhard Schölkopf, Abulhair Saparov, Mrinmaya Sachan:
Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners? CoRR abs/2401.18070 (2024) - [i353]Alice Bizeul, Bernhard Schölkopf, Carl Allen:
A Probabilistic Model to explain Self-Supervised Representation Learning. CoRR abs/2402.01399 (2024) - [i352]Alexander Song, Sai Nikhilesh Murty Kottapalli, Rahul Goyal, Bernhard Schölkopf, Peer Fischer:
Low-power scalable multilayer optoelectronic neural networks enabled with incoherent light. CoRR abs/2402.01988 (2024) - [i351]Jonathan Thomm, Aleksandar Terzic, Geethan Karunaratne, Giacomo Camposampiero, Bernhard Schölkopf, Abbas Rahimi:
Limits of Transformer Language Models on Learning Algorithmic Compositions. CoRR abs/2402.05785 (2024) - [i350]Tarun Gupta, Wenbo Gong, Chao Ma, Nick Pawlowski, Agrin Hilmkil, Meyer Scetbon, Ade Famoti, Ashley Juan Llorens, Jianfeng Gao, Stefan Bauer, Danica Kragic, Bernhard Schölkopf, Cheng Zhang:
The Essential Role of Causality in Foundation World Models for Embodied AI. CoRR abs/2402.06665 (2024) - [i349]Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar:
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models. CoRR abs/2402.09236 (2024) - [i348]Francesco Ortu, Zhijing Jin, Diego Doimo, Mrinmaya Sachan, Alberto Cazzaniga, Bernhard Schölkopf:
Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals. CoRR abs/2402.11655 (2024) - [i347]Hsiao-Ru Pan, Bernhard Schölkopf:
Skill or Luck? Return Decomposition via Advantage Functions. CoRR abs/2402.12874 (2024) - [i346]Adithya Kumar Chinnakkonda Ravi, Victor Dhédin, Armand Jordana, Huaijiang Zhu, Avadesh Meduri, Ludovic Righetti, Bernhard Schölkopf, Majid Khadiv:
Efficient Search and Learning for Agile Locomotion on Stepping Stones. CoRR abs/2403.03639 (2024) - [i345]Sidak Pal Singh, Bobby He, Thomas Hofmann, Bernhard Schölkopf:
Hallmarks of Optimization Trajectories in Neural Networks and LLMs: The Lengths, Bends, and Dead Ends. CoRR abs/2403.07379 (2024) - [i344]Yaxi Hu, Amartya Sanyal, Bernhard Schölkopf:
Provable Privacy with Non-Private Pre-Processing. CoRR abs/2403.13041 (2024) - [i343]Nasim Rahaman, Martin Weiss, Manuel Wüthrich, Yoshua Bengio, Li Erran Li, Chris Pal, Bernhard Schölkopf:
Language Models Can Reduce Asymmetry in Information Markets. CoRR abs/2403.14443 (2024) - [i342]Felix Leeb, Bernhard Schölkopf:
A diverse Multilingual News Headlines Dataset from around the World. CoRR abs/2403.19352 (2024) - [i341]Zhiheng Lyu, Zhijing Jin, Fernando Gonzalez, Rada Mihalcea, Bernhard Schölkopf, Mrinmaya Sachan:
On the Causal Nature of Sentiment Analysis. CoRR abs/2404.11055 (2024) - [i340]Anson Lei, Frederik Nolte, Bernhard Schölkopf, Ingmar Posner:
Compete and Compose: Learning Independent Mechanisms for Modular World Models. CoRR abs/2404.15109 (2024) - [i339]Giorgio Piatti, Zhijing Jin, Max Kleiman-Weiner, Bernhard Schölkopf, Mrinmaya Sachan, Rada Mihalcea:
Cooperate or Collapse: Emergence of Sustainability Behaviors in a Society of LLM Agents. CoRR abs/2404.16698 (2024) - [i338]Zhijing Jin, Yuen Chen, Fernando Gonzalez, Jiarui Liu, Jiayi Zhang, Julian Michael, Bernhard Schölkopf, Mona T. Diab:
Analyzing the Role of Semantic Representations in the Era of Large Language Models. CoRR abs/2405.01502 (2024) - [i337]Heiner Kremer, Bernhard Schölkopf:
Geometry-Aware Instrumental Variable Regression. CoRR abs/2405.11633 (2024) - [i336]Zhijing Jin, Nils Heil, Jiarui Liu, Shehzaad Dhuliawala, Yahang Qi, Bernhard Schölkopf, Rada Mihalcea, Mrinmaya Sachan:
Implicit Personalization in Language Models: A Systematic Study. CoRR abs/2405.14808 (2024) - [i335]Siyuan Guo, Aniket Didolkar, Nan Rosemary Ke, Anirudh Goyal, Ferenc Huszár, Bernhard Schölkopf:
Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs. CoRR abs/2405.15485 (2024) - [i334]Siyuan Guo, Chi Zhang, Karthika Mohan, Ferenc Huszár, Bernhard Schölkopf:
Do Finetti: On Causal Effects for Exchangeable Data. CoRR abs/2405.18836 (2024) - [i333]Roberto Ceraolo, Dmitrii Kharlapenko, Amélie Reymond, Rada Mihalcea, Mrinmaya Sachan, Bernhard Schölkopf, Zhijing Jin:
CausalQuest: Collecting Natural Causal Questions for AI Agents. CoRR abs/2405.20318 (2024) - [i332]Robin Chan, Reda Boumasmoud, Anej Svete, Yuxin Ren, Qipeng Guo, Zhijing Jin, Shauli Ravfogel, Mrinmaya Sachan, Bernhard Schölkopf, Mennatallah El-Assady, Ryan Cotterell:
On Affine Homotopy between Language Encoders. CoRR abs/2406.02329 (2024) - [i331]Tim Z. Xiao, Robert Bamler, Bernhard Schölkopf, Weiyang Liu:
Verbalized Machine Learning: Revisiting Machine Learning with Language Models. CoRR abs/2406.04344 (2024) - [i330]Weronika Ormaniec, Scott Sussex, Lars Lorch, Bernhard Schölkopf, Andreas Krause:
Standardizing Structural Causal Models. CoRR abs/2406.11601 (2024) - [i329]Patrik Reizinger, Siyuan Guo, Ferenc Huszár, Bernhard Schölkopf, Wieland Brendel:
Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning. CoRR abs/2406.14302 (2024) - [i328]Sidak Pal Singh, Linara Adilova, Michael Kamp, Asja Fischer, Bernhard Schölkopf, Thomas Hofmann:
Landscaping Linear Mode Connectivity. CoRR abs/2406.16300 (2024) - [i327]Alizée Pace, Bernhard Schölkopf, Gunnar Rätsch, Giorgia Ramponi:
Preference Elicitation for Offline Reinforcement Learning. CoRR abs/2406.18450 (2024) - [i326]Amartya Sanyal, Yaxi Hu, Yaodong Yu, Yian Ma, Yixin Wang, Bernhard Schölkopf:
Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation. CoRR abs/2406.19049 (2024) - [i325]Shaobo Cui, Zhijing Jin, Bernhard Schölkopf, Boi Faltings:
The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning. CoRR abs/2406.19307 (2024) - [i324]Yujia Zheng, Zeyu Tang, Yiwen Qiu, Bernhard Schölkopf, Kun Zhang:
Detecting and Identifying Selection Structure in Sequential Data. CoRR abs/2407.00529 (2024) - [i323]Jonathan Thomm, Michael Hersche, Giacomo Camposampiero, Aleksandar Terzic, Bernhard Schölkopf, Abbas Rahimi:
Terminating Differentiable Tree Experts. CoRR abs/2407.02060 (2024) - [i322]Zhijing Jin, Sydney Levine, Max Kleiman-Weiner, Giorgio Piatti, Jiarui Liu, Fernando Gonzalez Adauto, Francesco Ortu, András Strausz, Mrinmaya Sachan, Rada Mihalcea, Yejin Choi, Bernhard Schölkopf:
Multilingual Trolley Problems for Language Models. CoRR abs/2407.02273 (2024) - [i321]Maximilian Dax, Stephen R. Green, Jonathan Gair, Nihar Gupte, Michael Pürrer, Vivien Raymond, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf:
Real-time gravitational-wave inference for binary neutron stars using machine learning. CoRR abs/2407.09602 (2024) - [i320]Zeju Qiu, Weiyang Liu, Haiwen Feng, Zhen Liu, Tim Z. Xiao, Katherine M. Collins, Joshua B. Tenenbaum, Adrian Weller, Michael J. Black, Bernhard Schölkopf:
Can Large Language Models Understand Symbolic Graphics Programs? CoRR abs/2408.08313 (2024) - [i319]Yi Zhao, Le Chen, Jan Schneider, Quankai Gao, Juho Kannala, Bernhard Schölkopf, Joni Pajarinen, Dieter Büchler:
RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands. CoRR abs/2408.11048 (2024) - 2023
- [i318]Yuejiang Liu, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow, Bernhard Schölkopf, Francesco Locatello:
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning. CoRR abs/2301.05169 (2023) - [i317]Flavio Schneider, Zhijing Jin, Bernhard Schölkopf:
Moûsai: Text-to-Music Generation with Long-Context Latent Diffusion. CoRR abs/2301.11757 (2023) - [i316]Soledad Villar, David W. Hogg, Weichi Yao, George A. Kevrekidis, Bernhard Schölkopf:
The passive symmetries of machine learning. CoRR abs/2301.13724 (2023) - [i315]Ahmad-Reza Ehyaei, Amir-Hossein Karimi, Bernhard Schölkopf, Setareh Maghsudi:
Robustness Implies Fairness in Causal Algorithmic Recourse. CoRR abs/2302.03465 (2023) - [i314]Jonas Wildberger, Siyuan Guo, Arnab Bhattacharyya, Bernhard Schölkopf:
On the Interventional Kullback-Leibler Divergence. CoRR abs/2302.05380 (2023) - [i313]Uddeshya Upadhyay, Jae-Myung Kim, Cordelia Schmid, Bernhard Schölkopf, Zeynep Akata:
Posterior Annealing: Fast Calibrated Uncertainty for Regression. CoRR abs/2302.11012 (2023) - [i312]Vincent Stimper, David Liu, Andrew Campbell, Vincent Berenz, Lukas Ryll, Bernhard Schölkopf, José Miguel Hernández-Lobato:
normflows: A PyTorch Package for Normalizing Flows. CoRR abs/2302.12014 (2023) - [i311]Simon Guist, Jan Schneider, Alexander Dittrich, Vincent Berenz, Bernhard Schölkopf, Dieter Büchler:
Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning. CoRR abs/2303.02234 (2023) - [i310]Weiyang Liu, Longhui Yu, Adrian Weller, Bernhard Schölkopf:
Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap. CoRR abs/2303.06484 (2023) - [i309]Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence:
Dataflow graphs as complete causal graphs. CoRR abs/2303.09552 (2023) - [i308]Siyuan Guo, Jonas Wildberger, Bernhard Schölkopf:
Out-of-Variable Generalization. CoRR abs/2304.07896 (2023) - [i307]Marco Fumero, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, Francesco Locatello:
Leveraging sparse and shared feature activations for disentangled representation learning. CoRR abs/2304.07939 (2023) - [i306]Zhiheng Lyu, Zhijing Jin, Justus Mattern, Rada Mihalcea, Mrinmaya Sachan, Bernhard Schölkopf:
Psychologically-Inspired Causal Prompts. CoRR abs/2305.01764 (2023) - [i305]Fernando Gonzalez, Zhijing Jin, Bernhard Schölkopf, Tom Hope, Mrinmaya Sachan, Rada Mihalcea:
Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good. CoRR abs/2305.05471 (2023) - [i304]Sidak Pal Singh, Thomas Hofmann, Bernhard Schölkopf:
The Hessian perspective into the Nature of Convolutional Neural Networks. CoRR abs/2305.09088 (2023) - [i303]Heiner Kremer, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu:
Estimation Beyond Data Reweighting: Kernel Method of Moments. CoRR abs/2305.10898 (2023) - [i302]Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel:
Provably Learning Object-Centric Representations. CoRR abs/2305.14229 (2023) - [i301]Yuxin Ren, Qipeng Guo, Zhijing Jin, Shauli Ravfogel, Mrinmaya Sachan, Bernhard Schölkopf, Ryan Cotterell:
All Roads Lead to Rome? Exploring the Invariance of Transformers' Representations. CoRR abs/2305.14555 (2023) - [i300]Yiwen Ding, Jiarui Liu, Zhiheng Lyu, Kun Zhang, Bernhard Schölkopf, Zhijing Jin, Rada Mihalcea:
Voices of Her: Analyzing Gender Differences in the AI Publication World. CoRR abs/2305.14597 (2023) - [i299]Junhyung Park, Simon Buchholz, Bernhard Schölkopf, Krikamol Muandet:
A Measure-Theoretic Axiomatisation of Causality. CoRR abs/2305.17139 (2023) - [i298]Maximilian Dax, Jonas Wildberger, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf:
Flow Matching for Scalable Simulation-Based Inference. CoRR abs/2305.17161 (2023) - [i297]Wendong Liang, Armin Kekic, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, Bernhard Schölkopf:
Causal Component Analysis. CoRR abs/2305.17225 (2023) - [i296]Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schölkopf, Mrinmaya Sachan, Taylor Berg-Kirkpatrick:
Membership Inference Attacks against Language Models via Neighbourhood Comparison. CoRR abs/2305.18462 (2023) - [i295]Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf:
Nonparametric Identifiability of Causal Representations from Unknown Interventions. CoRR abs/2306.00542 (2023) - [i294]Alizée Pace, Hugo Yèche, Bernhard Schölkopf, Gunnar Rätsch, Guy Tennenholtz:
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding. CoRR abs/2306.01157 (2023) - [i293]Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar:
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. CoRR abs/2306.02235 (2023) - [i292]Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf:
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. CoRR abs/2306.03968 (2023) - [i291]Zhijing Jin, Jiarui Liu, Zhiheng Lyu, Spencer Poff, Mrinmaya Sachan, Rada Mihalcea, Mona T. Diab, Bernhard Schölkopf:
Can Large Language Models Infer Causation from Correlation? CoRR abs/2306.05836 (2023) - [i290]Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach:
Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimators. CoRR abs/2306.06002 (2023) - [i289]Zeju Qiu, Weiyang Liu, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, Bernhard Schölkopf:
Controlling Text-to-Image Diffusion by Orthogonal Finetuning. CoRR abs/2306.07280 (2023) - [i288]Vincent Berenz, Felix Widmaier, Simon Guist, Bernhard Schölkopf, Dieter Büchler:
Synchronizing Machine Learning Algorithms, Realtime Robotic Control and Simulated Environment with o80. CoRR abs/2306.09764 (2023) - [i287]Aaron Spieler, Nasim Rahaman, Georg Martius, Bernhard Schölkopf, Anna Levina:
The ELM Neuron: an Efficient and Expressive Cortical Neuron Model Can Solve Long-Horizon Tasks. CoRR abs/2306.16922 (2023) - [i286]Simon Guist, Jan Schneider, Hao Ma, Vincent Berenz, Julian Martus, Felix Grüninger, Michael Mühlebach, Jonathan Fiene, Bernhard Schölkopf, Dieter Büchler:
A Robust Open-source Tendon-driven Robot Arm for Learning Control of Dynamic Motions. CoRR abs/2307.02654 (2023) - [i285]Cian Eastwood, Shashank Singh, Andrei Liviu Nicolicioiu, Marin Vlastelica, Julius von Kügelgen, Bernhard Schölkopf:
Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features. CoRR abs/2307.09933 (2023) - [i284]Nico Gürtler, Sebastian Blaes, Pavel Kolev, Felix Widmaier, Manuel Wüthrich, Stefan Bauer, Bernhard Schölkopf, Georg Martius:
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware. CoRR abs/2307.15690 (2023) - [i283]Nico Gürtler, Felix Widmaier, Cansu Sancaktar, Sebastian Blaes, Pavel Kolev, Stefan Bauer, Manuel Wüthrich, Markus Wulfmeier, Martin A. Riedmiller, Arthur Allshire, Qiang Wang, Robert McCarthy, Hangyeol Kim, Jongchan Baek, Wookyong Kwon, Shanliang Qian, Yasunori Toshimitsu, Mike Yan Michelis, Amirhossein Kazemipour, Arman Raayatsanati, Hehui Zheng, Barnabas Gavin Cangan, Bernhard Schölkopf, Georg Martius:
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World. CoRR abs/2308.07741 (2023) - [i282]Laurence I. Midgley, Vincent Stimper, Javier Antorán, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato:
SE(3) Equivariant Augmented Coupling Flows. CoRR abs/2308.10364 (2023) - [i281]Philip Tobuschat, Hao Ma, Dieter Büchler, Bernhard Schölkopf, Michael Muehlebach:
Data-Efficient Online Learning of Ball Placement in Robot Table Tennis. CoRR abs/2308.14562 (2023) - [i280]Timothy D. Gebhard, Daniel Angerhausen, Björn S. Konrad, Eleonora Alei, Sascha P. Quanz, Bernhard Schölkopf:
Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networks. CoRR abs/2309.03075 (2023) - [i279]Jan Schneider, Pierre Schumacher, Daniel F. B. Häufle, Bernhard Schölkopf, Dieter Büchler:
Investigating the Impact of Action Representations in Policy Gradient Algorithms. CoRR abs/2309.06921 (2023) - [i278]Léon Bottou, Bernhard Schölkopf:
Borges and AI. CoRR abs/2310.01425 (2023) - [i277]Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach:
Deep Backtracking Counterfactuals for Causally Compliant Explanations. CoRR abs/2310.07665 (2023) - [i276]Open X.-Embodiment Collaboration, Abhishek Padalkar, Acorn Pooley, Ajinkya Jain, Alex Bewley, Alexander Herzog, Alex Irpan, Alexander Khazatsky, Anant Raj, Anikait Singh, Anthony Brohan, Antonin Raffin, Ayzaan Wahid, Ben Burgess-Limerick, Beomjoon Kim, Bernhard Schölkopf, Brian Ichter, Cewu Lu, Charles Xu, Chelsea Finn, Chenfeng Xu, Cheng Chi, Chenguang Huang, Christine Chan, Chuer Pan, Chuyuan Fu, Coline Devin, Danny Driess, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Federico Ceola, Fei Xia, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Ge Yan, Giulio Schiavi, Gregory Kahn, Hao Su, Haoshu Fang, Haochen Shi, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Igor Mordatch, Ilija Radosavovic, et al.:
Open X-Embodiment: Robotic Learning Datasets and RT-X Models. CoRR abs/2310.08864 (2023) - [i275]Yandong Wen, Weiyang Liu, Yao Feng, Bhiksha Raj, Rita Singh, Adrian Weller, Michael J. Black, Bernhard Schölkopf:
Pairwise Similarity Learning is SimPLE. CoRR abs/2310.09449 (2023) - [i274]Zhen Liu, Yao Feng, Yuliang Xiu, Weiyang Liu, Liam Paull, Michael J. Black, Bernhard Schölkopf:
Ghost on the Shell: An Expressive Representation of General 3D Shapes. CoRR abs/2310.15168 (2023) - [i273]Lars Lorch, Andreas Krause, Bernhard Schölkopf:
Causal Modeling with Stationary Diffusions. CoRR abs/2310.17405 (2023) - [i272]Ishan Kumar, Zhijing Jin, Ehsan Mokhtarian, Siyuan Guo, Yuen Chen, Negar Kiyavash, Mrinmaya Sachan, Bernhard Schölkopf:
CausalCite: A Causal Formulation of Paper Citations. CoRR abs/2311.02790 (2023) - [i271]Weiyang Liu, Zeju Qiu, Yao Feng, Yuliang Xiu, Yuxuan Xue, Longhui Yu, Haiwen Feng, Zhen Liu, Juyeon Heo, Songyou Peng, Yandong Wen, Michael J. Black, Adrian Weller, Bernhard Schölkopf:
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization. CoRR abs/2311.06243 (2023) - [i270]David F. Jenny, Yann Billeter, Mrinmaya Sachan, Bernhard Schölkopf, Zhijing Jin:
Navigating the Ocean of Biases: Political Bias Attribution in Language Models via Causal Structures. CoRR abs/2311.08605 (2023) - [i269]Cian Eastwood, Julius von Kügelgen, Linus Ericsson, Diane Bouchacourt, Pascal Vincent, Bernhard Schölkopf, Mark Ibrahim:
Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations. CoRR abs/2311.08815 (2023) - [i268]Armin Kekic, Bernhard Schölkopf, Michel Besserve:
Targeted Reduction of Causal Models. CoRR abs/2311.18639 (2023) - [i267]Gege Gao, Weiyang Liu, Anpei Chen, Andreas Geiger, Bernhard Schölkopf:
GraphDreamer: Compositional 3D Scene Synthesis from Scene Graphs. CoRR abs/2312.00093 (2023) - [i266]Zhijing Jin, Yuen Chen, Felix Leeb, Luigi Gresele, Ojasv Kamal, Zhiheng Lyu, Kevin Blin, Fernando Gonzalez Adauto, Max Kleiman-Weiner, Mrinmaya Sachan, Bernhard Schölkopf:
CLadder: A Benchmark to Assess Causal Reasoning Capabilities of Language Models. CoRR abs/2312.04350 (2023) - [i265]Timothy D. Gebhard, Jonas Wildberger, Maximilian Dax, Daniel Angerhausen, Sascha P. Quanz, Bernhard Schölkopf:
Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling. CoRR abs/2312.08295 (2023) - [i264]Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen, Michel Besserve, Bernhard Schölkopf:
Independent Mechanism Analysis and the Manifold Hypothesis. CoRR abs/2312.13438 (2023) - 2022
- [i263]Arash Mehrjou, Ashkan Soleymani, Stefan Bauer, Bernhard Schölkopf:
Physical Derivatives: Computing policy gradients by physical forward-propagation. CoRR abs/2201.05830 (2022) - [i262]Davide Mambelli, Frederik Träuble, Stefan Bauer, Bernhard Schölkopf, Francesco Locatello:
Compositional Multi-Object Reinforcement Learning with Linear Relation Networks. CoRR abs/2201.13388 (2022) - [i261]Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing:
Causal Inference Through the Structural Causal Marginal Problem. CoRR abs/2202.01300 (2022) - [i260]Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen, Michel Besserve, Bernhard Schölkopf:
On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective". CoRR abs/2202.06844 (2022) - [i259]Zhijing Jin, Abhinav Lalwani, Tejas Vaidhya, Xiaoyu Shen, Yiwen Ding, Zhiheng Lyu, Mrinmaya Sachan, Rada Mihalcea, Bernhard Schölkopf:
Logical Fallacy Detection. CoRR abs/2202.13758 (2022) - [i258]Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer:
Interventions, Where and How? Experimental Design for Causal Models at Scale. CoRR abs/2203.02016 (2022) - [i257]Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russell, Bernhard Schölkopf, Dominik Janzing, Francesco Locatello:
Score matching enables causal discovery of nonlinear additive noise models. CoRR abs/2203.04413 (2022) - [i256]Dominik Zietlow, Michael Lohaus, Guha Balakrishnan, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Chris Russell:
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers. CoRR abs/2203.04913 (2022) - [i255]Sidak Pal Singh, Aurélien Lucchi, Thomas Hofmann, Bernhard Schölkopf:
Phenomenology of Double Descent in Finite-Width Neural Networks. CoRR abs/2203.07337 (2022) - [i254]Siyuan Guo, Viktor Tóth, Bernhard Schölkopf, Ferenc Huszár:
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data. CoRR abs/2203.15756 (2022) - [i253]Bernhard Schölkopf, Julius von Kügelgen:
From Statistical to Causal Learning. CoRR abs/2204.00607 (2022) - [i252]Timothy D. Gebhard, Markus J. Bonse, Sascha P. Quanz, Bernhard Schölkopf:
Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal framework. CoRR abs/2204.03439 (2022) - [i251]Yassine Nemmour, Heiner Kremer, Bernhard Schölkopf, Jia-Jie Zhu:
Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee. CoRR abs/2204.11564 (2022) - [i250]Jingwei Ni, Zhijing Jin, Markus Freitag, Mrinmaya Sachan, Bernhard Schölkopf:
Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance. CoRR abs/2205.02293 (2022) - [i249]Lars Lorch, Scott Sussex, Jonas Rothfuss, Andreas Krause, Bernhard Schölkopf:
Amortized Inference for Causal Structure Learning. CoRR abs/2205.12934 (2022) - [i248]Alexander Hägele, Jonas Rothfuss, Lars Lorch, Vignesh Ram Somnath, Bernhard Schölkopf, Andreas Krause:
BaCaDI: Bayesian Causal Discovery with Unknown Interventions. CoRR abs/2206.01665 (2022) - [i247]Ronan Perry, Julius von Kügelgen, Bernhard Schölkopf:
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis. CoRR abs/2206.02013 (2022) - [i246]Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve:
Embrace the Gap: VAEs Perform Independent Mechanism Analysis. CoRR abs/2206.02416 (2022) - [i245]Aniket Das, Bernhard Schölkopf, Michael Muehlebach:
Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization. CoRR abs/2206.02953 (2022) - [i244]Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf:
AutoML Two-Sample Test. CoRR abs/2206.08843 (2022) - [i243]Anson Lei, Bernhard Schölkopf, Ingmar Posner:
Variational Causal Dynamics: Discovering Modular World Models from Interventions. CoRR abs/2206.11131 (2022) - [i242]Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf:
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions. CoRR abs/2207.04771 (2022) - [i241]Joanna Sliwa, Shubhangi Ghosh, Vincent Stimper, Luigi Gresele, Bernhard Schölkopf:
Probing the Robustness of Independent Mechanism Analysis for Representation Learning. CoRR abs/2207.06137 (2022) - [i240]Florian Wenzel, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, Francesco Locatello:
Assaying Out-Of-Distribution Generalization in Transfer Learning. CoRR abs/2207.09239 (2022) - [i239]Cian Eastwood, Alexander Robey, Shashank Singh, Julius von Kügelgen, Hamed Hassani, George J. Pappas, Bernhard Schölkopf:
Probable Domain Generalization via Quantile Risk Minimization. CoRR abs/2207.09944 (2022) - [i238]Weiyang Liu, Zhen Liu, Liam Paull, Adrian Weller, Bernhard Schölkopf:
Structural Causal 3D Reconstruction. CoRR abs/2207.10156 (2022) - [i237]Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Mozer, Kenji Kawaguchi, Yoshua Bengio, Bernhard Schölkopf:
Discrete Key-Value Bottleneck. CoRR abs/2207.11240 (2022) - [i236]Hamza Keurti, Hsiao-Ru Pan, Michel Besserve, Benjamin F. Grewe, Bernhard Schölkopf:
Homomorphism Autoencoder - Learning Group Structured Representations from Observed Transitions. CoRR abs/2207.12067 (2022) - [i235]Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, Bernhard Schölkopf, José Miguel Hernández-Lobato:
Flow Annealed Importance Sampling Bootstrap. CoRR abs/2208.01893 (2022) - [i234]Simon Buchholz, Michel Besserve, Bernhard Schölkopf:
Function Classes for Identifiable Nonlinear Independent Component Analysis. CoRR abs/2208.06406 (2022) - [i233]Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel, Tong He, Zheng Zhang, Bernhard Schölkopf, Thomas Brox, Francesco Locatello:
Bridging the Gap to Real-World Object-Centric Learning. CoRR abs/2209.14860 (2022) - [i232]Cian Eastwood, Andrei Liviu Nicolicioiu, Julius von Kügelgen, Armin Kekic, Frederik Träuble, Andrea Dittadi, Bernhard Schölkopf:
DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability. CoRR abs/2210.00364 (2022) - [i231]Zhijing Jin, Sydney Levine, Fernando Gonzalez, Ojasv Kamal, Maarten Sap, Mrinmaya Sachan, Rada Mihalcea, Josh Tenenbaum, Bernhard Schölkopf:
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment. CoRR abs/2210.01478 (2022) - [i230]Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf:
Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference. CoRR abs/2210.05686 (2022) - [i229]Alexander Dittrich, Jan Schneider, Simon Guist, Bernhard Schölkopf, Dieter Büchler:
AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation. CoRR abs/2210.06048 (2022) - [i228]Nasim Rahaman, Martin Weiss, Francesco Locatello, Chris Pal, Yoshua Bengio, Bernhard Schölkopf, Li Erran Li, Nicolas Ballas:
Neural Attentive Circuits. CoRR abs/2210.08031 (2022) - [i227]Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx:
On the Identifiability and Estimation of Causal Location-Scale Noise Models. CoRR abs/2210.09054 (2022) - [i226]Alessandro Stolfo, Zhijing Jin, Kumar Shridhar, Bernhard Schölkopf, Mrinmaya Sachan:
A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models. CoRR abs/2210.12023 (2022) - [i225]Justus Mattern, Zhijing Jin, Benjamin Weggenmann, Bernhard Schölkopf, Mrinmaya Sachan:
Differentially Private Language Models for Secure Data Sharing. CoRR abs/2210.13918 (2022) - [i224]Ziyu Wang, Yucen Luo, Yueru Li, Jun Zhu, Bernhard Schölkopf:
Spectral Representation Learning for Conditional Moment Models. CoRR abs/2210.16525 (2022) - [i223]Zeju Qiu, Weiyang Liu, Tim Z. Xiao, Zhen Liu, Umang Bhatt, Yucen Luo, Adrian Weller, Bernhard Schölkopf:
Iterative Teaching by Data Hallucination. CoRR abs/2210.17467 (2022) - [i222]Nasim Rahaman, Martin Weiss, Frederik Träuble, Francesco Locatello, Alexandre Lacoste, Yoshua Bengio, Chris Pal, Li Erran Li, Bernhard Schölkopf:
A General Purpose Neural Architecture for Geospatial Systems. CoRR abs/2211.02348 (2022) - [i221]Amin Abyaneh, Nino Scherrer, Patrick Schwab, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou:
FED-CD: Federated Causal Discovery from Interventional and Observational Data. CoRR abs/2211.03846 (2022) - [i220]Jonas Wildberger, Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf:
Adapting to noise distribution shifts in flow-based gravitational-wave inference. CoRR abs/2211.08801 (2022) - [i219]Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim:
On the Relationship Between Explanation and Prediction: A Causal View. CoRR abs/2212.06925 (2022) - [i218]Armin Kekic, Jonas Dehning, Luigi Gresele, Julius von Kügelgen, Viola Priesemann, Bernhard Schölkopf:
Evaluating vaccine allocation strategies using simulation-assisted causal modelling. CoRR abs/2212.08498 (2022) - [i217]Justus Mattern, Zhijing Jin, Mrinmaya Sachan, Rada Mihalcea, Bernhard Schölkopf:
Understanding Stereotypes in Language Models: Towards Robust Measurement and Zero-Shot Debiasing. CoRR abs/2212.10678 (2022) - 2021
- [i216]Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet:
An Optimal Witness Function for Two-Sample Testing. CoRR abs/2102.05573 (2021) - [i215]Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis:
Bayesian Quadrature on Riemannian Data Manifolds. CoRR abs/2102.06645 (2021) - [i214]Junhyung Park, Uri Shalit, Bernhard Schölkopf, Krikamol Muandet:
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression. CoRR abs/2102.08208 (2021) - [i213]Jia-Jie Zhu, Yassine Nemmour, Bernhard Schölkopf:
From Majorization to Interpolation: Distributionally Robust Learning using Kernel Smoothing. CoRR abs/2102.08474 (2021) - [i212]Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, Yoshua Bengio:
Towards Causal Representation Learning. CoRR abs/2102.11107 (2021) - [i211]Maximilian Mordig, Riccardo Della Vecchia, Nicolò Cesa-Bianchi, Bernhard Schölkopf:
Multi-Sided Matching Markets with Consistent Preferences and Cooperative Partners. CoRR abs/2102.11834 (2021) - [i210]Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf:
Nonlinear Invariant Risk Minimization: A Causal Approach. CoRR abs/2102.12353 (2021) - [i209]Weiyang Liu, Rongmei Lin, Zhen Liu, Li Xiong, Bernhard Schölkopf, Adrian Weller:
Learning with Hyperspherical Uniformity. CoRR abs/2103.01649 (2021) - [i208]Georgios Arvanitidis, Bogdan Georgiev, Bernhard Schölkopf:
A prior-based approximate latent Riemannian metric. CoRR abs/2103.05290 (2021) - [i207]Arash Mehrjou, Ashkan Soleymani, Amin Abyaneh, Bernhard Schölkopf, Stefan Bauer:
Pyfectious: An individual-level simulator to discover optimal containment polices for epidemic diseases. CoRR abs/2103.15561 (2021) - [i206]Manuel Wüthrich, Bernhard Schölkopf, Andreas Krause:
Regret Bounds for Gaussian-Process Optimization in Large Domains. CoRR abs/2104.14113 (2021) - [i205]Kanika Madan, Nan Rosemary Ke, Anirudh Goyal, Bernhard Schölkopf, Yoshua Bengio:
Fast and Slow Learning of Recurrent Independent Mechanisms. CoRR abs/2105.08710 (2021) - [i204]Lars Lorch, Jonas Rothfuss, Bernhard Schölkopf, Andreas Krause:
DiBS: Differentiable Bayesian Structure Learning. CoRR abs/2105.11839 (2021) - [i203]Rui Zhang, Krikamol Muandet, Bernhard Schölkopf, Masaaki Imaizumi:
Instrument Space Selection for Kernel Maximum Moment Restriction. CoRR abs/2106.03340 (2021) - [i202]Maximilian Seitzer, Bernhard Schölkopf, Georg Martius:
Causal Influence Detection for Improving Efficiency in Reinforcement Learning. CoRR abs/2106.03443 (2021) - [i201]Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello:
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. CoRR abs/2106.04619 (2021) - [i200]Luigi Gresele, Julius von Kügelgen, Vincent Stimper, Bernhard Schölkopf, Michel Besserve:
Independent mechanism analysis, a new concept? CoRR abs/2106.05200 (2021) - [i199]Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang:
Adversarial Robustness through the Lens of Causality. CoRR abs/2106.06196 (2021) - [i198]Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, Peter V. Gehler:
Towards Total Recall in Industrial Anomaly Detection. CoRR abs/2106.08265 (2021) - [i197]Julius von Kügelgen, Nikita Agarwal, Jakob Zeitler, Afsaneh Mastouri, Bernhard Schölkopf:
Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects. CoRR abs/2106.11849 (2021) - [i196]Maximilian Dax, Stephen R. Green, Jonathan Gair, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf:
Real-time gravitational-wave science with neural posterior estimation. CoRR abs/2106.12594 (2021) - [i195]Diego Agudelo-España, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu:
Shallow Representation is Deep: Learning Uncertainty-aware and Worst-case Random Feature Dynamics. CoRR abs/2106.13066 (2021) - [i194]Felix Leeb, Stefan Bauer, Bernhard Schölkopf:
Interventional Assays for the Latent Space of Autoencoders. CoRR abs/2106.16091 (2021) - [i193]Andrea Dittadi, Samuele Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, Francesco Locatello:
Generalization and Robustness Implications in Object-Centric Learning. CoRR abs/2107.00637 (2021) - [i192]Frederik Träuble, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Peter V. Gehler:
Backward-Compatible Prediction Updates: A Probabilistic Approach. CoRR abs/2107.01057 (2021) - [i191]Cian Eastwood, Ian Mason, Christopher K. I. Williams, Bernhard Schölkopf:
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration. CoRR abs/2107.05446 (2021) - [i190]Andrea Dittadi, Frederik Träuble, Manuel Wüthrich, Felix Widmaier, Peter V. Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer:
Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning. CoRR abs/2107.05686 (2021) - [i189]Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter V. Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel:
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. CoRR abs/2107.08221 (2021) - [i188]Nino Scherrer, Olexa Bilaniuk, Yashas Annadani, Anirudh Goyal, Patrick Schwab, Bernhard Schölkopf, Michael C. Mozer, Yoshua Bengio, Stefan Bauer, Nan Rosemary Ke:
Learning Neural Causal Models with Active Interventions. CoRR abs/2109.02429 (2021) - [i187]Hsiao-Ru Pan, Nico Gürtler, Alexander Neitz, Bernhard Schölkopf:
Direct Advantage Estimation. CoRR abs/2109.06093 (2021) - [i186]Stefan Bauer, Felix Widmaier, Manuel Wüthrich, Niklas Funk, Julen Urain De Jesus, Jan Peters, Joe Watson, Claire Chen, Krishnan Srinivasan, Junwu Zhang, Jeffrey Zhang, Matthew R. Walter, Rishabh Madan, Charles B. Schaff, Takahiro Maeda, Takuma Yoneda, Denis Yarats, Arthur Allshire, Ethan K. Gordon, Tapomayukh Bhattacharjee, Siddhartha S. Srinivasa, Animesh Garg, Annika Buchholz, Sebastian Stark, Thomas Steinbrenner, Joel Akpo, Shruti Joshi, Vaibhav Agrawal, Bernhard Schölkopf:
A Robot Cluster for Reproducible Research in Dexterous Manipulation. CoRR abs/2109.10957 (2021) - [i185]Lukas Kondmann, Aysim Toker, Sudipan Saha, Bernhard Schölkopf, Laura Leal-Taixé, Xiaoxiang Zhu:
Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images. CoRR abs/2110.02068 (2021) - [i184]Zhijing Jin, Julius von Kügelgen, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, Bernhard Schölkopf:
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP. CoRR abs/2110.03618 (2021) - [i183]Yukun Chen, Frederik Träuble, Andrea Dittadi, Stefan Bauer, Bernhard Schölkopf:
Boxhead: A Dataset for Learning Hierarchical Representations. CoRR abs/2110.03628 (2021) - [i182]Osama Makansi, Julius von Kügelgen, Francesco Locatello, Peter V. Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf:
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. CoRR abs/2110.05304 (2021) - [i181]Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang:
Action-Sufficient State Representation Learning for Control with Structural Constraints. CoRR abs/2110.05721 (2021) - [i180]Nasim Rahaman, Muhammad Waleed Gondal, Shruti Joshi, Peter V. Gehler, Yoshua Bengio, Francesco Locatello, Bernhard Schölkopf:
Dynamic Inference with Neural Interpreters. CoRR abs/2110.06399 (2021) - [i179]Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter V. Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf:
Unsupervised Object Learning via Common Fate. CoRR abs/2110.06562 (2021) - [i178]Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu:
Distributional Robustness Regularized Scenario Optimization with Application to Model Predictive Control. CoRR abs/2110.13588 (2021) - [i177]Weiyang Liu, Zhen Liu, Hanchen Wang, Liam Paull, Bernhard Schölkopf, Adrian Weller:
Iterative Teaching by Label Synthesis. CoRR abs/2110.14432 (2021) - [i176]Sumedh A. Sontakke, Stephen Iota, Zizhao Hu, Arash Mehrjou, Laurent Itti, Bernhard Schölkopf:
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL. CoRR abs/2110.15489 (2021) - [i175]Vincent Stimper, Bernhard Schölkopf, José Miguel Hernández-Lobato:
Resampling Base Distributions of Normalizing Flows. CoRR abs/2110.15828 (2021) - [i174]Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, Jakob H. Macke:
Group equivariant neural posterior estimation. CoRR abs/2111.13139 (2021) - [i173]Hanlin Zhang, Yi-Fan Zhang, Weiyang Liu, Adrian Weller, Bernhard Schölkopf, Eric P. Xing:
Towards Principled Disentanglement for Domain Generalization. CoRR abs/2111.13839 (2021) - [i172]Michel Besserve, Bernhard Schölkopf:
Learning soft interventions in complex equilibrium systems. CoRR abs/2112.05729 (2021) - [i171]Ricardo Dominguez-Olmedo, Amir-Hossein Karimi, Bernhard Schölkopf:
On the Adversarial Robustness of Causal Algorithmic Recourse. CoRR abs/2112.11313 (2021) - 2020
- [i170]Jia-Jie Zhu, Bernhard Schölkopf, Moritz Diehl:
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control. CoRR abs/2001.10398 (2020) - [i169]Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen:
Weakly-Supervised Disentanglement Without Compromises. CoRR abs/2002.02886 (2020) - [i168]Amir-Hossein Karimi, Bernhard Schölkopf, Isabel Valera:
Algorithmic Recourse: from Counterfactual Explanations to Interventions. CoRR abs/2002.06278 (2020) - [i167]Wittawat Jitkrittum, Heishiro Kanagawa, Bernhard Schölkopf:
Testing Goodness of Fit of Conditional Density Models with Kernels. CoRR abs/2002.10271 (2020) - [i166]Matthias R. Hohmann, Lisa Konieczny, Michelle Hackl, Brian Wirth, Talha Zaman, Raffi Enficiaud, Moritz Grosse-Wentrup, Bernhard Schölkopf:
MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware. CoRR abs/2002.11754 (2020) - [i165]Emmanouil Angelis, Philippe Wenk, Bernhard Schölkopf, Stefan Bauer, Andreas Krause:
SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives. CoRR abs/2003.02658 (2020) - [i164]Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf:
Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem. CoRR abs/2004.00166 (2020) - [i163]Michel Besserve, Rémy Sun, Dominik Janzing, Bernhard Schölkopf:
A theory of independent mechanisms for extrapolation in generative models. CoRR abs/2004.00184 (2020) - [i162]Lars Lorch, William Trouleau, Stratis Tsirtsis, Aron Szanto, Bernhard Schölkopf, Manuel Gomez-Rodriguez:
A Spatiotemporal Epidemic Model to Quantify the Effects of Contact Tracing, Testing, and Containment. CoRR abs/2004.07641 (2020) - [i161]Julius von Kügelgen, Ivan Ustyuzhaninov, Peter V. Gehler, Matthias Bethge, Bernhard Schölkopf:
Towards causal generative scene models via competition of experts. CoRR abs/2004.12906 (2020) - [i160]Louis Abraham, Gary Bécigneul, Bernhard Schölkopf:
Crackovid: Optimizing Group Testing. CoRR abs/2005.06413 (2020) - [i159]Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet:
Learning Kernel Tests Without Data Splitting. CoRR abs/2006.02286 (2020) - [i158]Arash Mehrjou, Mohammad Ghavamzadeh, Bernhard Schölkopf:
Automatic Policy Synthesis to Improve the Safety of Nonlinear Dynamical Systems. CoRR abs/2006.03947 (2020) - [i157]Dieter Büchler, Simon Guist, Roberto Calandra, Vincent Berenz, Bernhard Schölkopf, Jan Peters:
Learning to Play Table Tennis From Scratch using Muscular Robots. CoRR abs/2006.05935 (2020) - [i156]Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera:
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach. CoRR abs/2006.06831 (2020) - [i155]Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf:
Kernel Distributionally Robust Optimization. CoRR abs/2006.06981 (2020) - [i154]Felix Leeb, Yashas Annadani, Stefan Bauer, Bernhard Schölkopf:
Structural Autoencoders Improve Representations for Generation and Transfer. CoRR abs/2006.07796 (2020) - [i153]Frederik Träuble, Elliot Creager, Niki Kilbertus, Anirudh Goyal, Francesco Locatello, Bernhard Schölkopf, Stefan Bauer:
Is Independence all you need? On the Generalization of Representations Learned from Correlated Data. CoRR abs/2006.07886 (2020) - [i152]Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvärinen:
Relative gradient optimization of the Jacobian term in unsupervised deep learning. CoRR abs/2006.15090 (2020) - [i151]Anant Raj, Stefan Bauer, Ashkan Soleymani, Michel Besserve, Bernhard Schölkopf:
Causal Feature Selection via Orthogonal Search. CoRR abs/2007.02938 (2020) - [i150]Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf:
S2RMs: Spatially Structured Recurrent Modules. CoRR abs/2007.06533 (2020) - [i149]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Commentary on the Unsupervised Learning of Disentangled Representations. CoRR abs/2007.14184 (2020) - [i148]Georgios Arvanitidis, Søren Hauberg, Bernhard Schölkopf:
Geometrically Enriched Latent Spaces. CoRR abs/2008.00565 (2020) - [i147]Louis Abraham, Gary Bécigneul, Benjamin Coleman, Bernhard Schölkopf, Anshumali Shrivastava, Alexander J. Smola:
Bloom Origami Assays: Practical Group Testing. CoRR abs/2008.02641 (2020) - [i146]Manuel Wüthrich, Felix Widmaier, Felix Grimminger, Joel Akpo, Shruti Joshi, Vaibhav Agrawal, Bilal Hammoud, Majid Khadiv, Miroslav Bogdanovic, Vincent Berenz, Julian Viereck, Maximilien Naveau, Ludovic Righetti, Bernhard Schölkopf, Stefan Bauer:
TriFinger: An Open-Source Robot for Learning Dexterity. CoRR abs/2008.03596 (2020) - [i145]Arash Mehrjou, Andrea Iannelli, Bernhard Schölkopf:
Learning Dynamical Systems using Local Stability Priors. CoRR abs/2008.10053 (2020) - [i144]Patrick Schwab, Arash Mehrjou, Sonali Parbhoo, Leo Anthony Celi, Jürgen Hetzel, Markus Hofer, Bernhard Schölkopf, Stefan Bauer:
Real-time Prediction of COVID-19 related Mortality using Electronic Health Records. CoRR abs/2008.13412 (2020) - [i143]Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, Bernhard Schölkopf:
Learning explanations that are hard to vary. CoRR abs/2009.00329 (2020) - [i142]Sumedh A. Sontakke, Arash Mehrjou, Laurent Itti, Bernhard Schölkopf:
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning. CoRR abs/2010.03110 (2020) - [i141]Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf, Isabel Valera:
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects. CoRR abs/2010.04050 (2020) - [i140]Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Manuel Wüthrich, Yoshua Bengio, Bernhard Schölkopf, Stefan Bauer:
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning. CoRR abs/2010.04296 (2020) - [i139]Timothy D. Gebhard, Markus J. Bonse, Sascha P. Quanz, Bernhard Schölkopf:
Physically constrained causal noise models for high-contrast imaging of exoplanets. CoRR abs/2010.05591 (2020) - [i138]Julius von Kügelgen, Umang Bhatt, Amir-Hossein Karimi, Isabel Valera, Adrian Weller, Bernhard Schölkopf:
On the Fairness of Causal Algorithmic Recourse. CoRR abs/2010.06529 (2020) - [i137]Muhammad Waleed Gondal, Shruti Joshi, Nasim Rahaman, Stefan Bauer, Manuel Wüthrich, Bernhard Schölkopf:
Function Contrastive Learning of Transferable Representations. CoRR abs/2010.07093 (2020) - [i136]Rui Zhang, Masaaki Imaizumi, Bernhard Schölkopf, Krikamol Muandet:
Maximum Moment Restriction for Instrumental Variable Regression. CoRR abs/2010.07684 (2020) - [i135]Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif B. Müller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilaniuk, David L. Buckeridge, Gaétan Marceau Caron, Pierre Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Chris Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams:
Predicting Infectiousness for Proactive Contact Tracing. CoRR abs/2010.12536 (2020) - [i134]Andrea Dittadi, Frederik Träuble, Francesco Locatello, Manuel Wüthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, Bernhard Schölkopf:
On the Transfer of Disentangled Representations in Realistic Settings. CoRR abs/2010.14407 (2020) - [i133]Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. CoRR abs/2010.14766 (2020) - [i132]Prateek Gupta, Tegan Maharaj, Martin Weiss, Nasim Rahaman, Hannah Alsdurf, Abhinav Sharma, Nanor Minoyan, Soren Harnois-Leblanc, Victor Schmidt, Pierre-Luc St-Charles, Tristan Deleu, Andrew Williams, Akshay Patel, Meng Qu, Olexa Bilaniuk, Gaétan Marceau Caron, Pierre Luc Carrier, Satya Ortiz-Gagné, Marc-Andre Rousseau, David L. Buckeridge, Joumana Ghosn, Yang Zhang, Bernhard Schölkopf, Jian Tang, Irina Rish, Christopher Joseph Pal, Joanna Merckx, Eilif B. Müller, Yoshua Bengio:
COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing. CoRR abs/2010.16004 (2020) - [i131]Gilles Barthe, Roberta De Viti, Peter Druschel, Deepak Garg, Manuel Gomez-Rodriguez, Pierfrancesco Ingo, Matthew Lentz, Aastha Mehta, Bernhard Schölkopf:
PanCast: Listening to Bluetooth Beacons for Epidemic Risk Mitigation. CoRR abs/2011.08069 (2020) - [i130]Chaochao Lu, Biwei Huang, Ke Wang, José Miguel Hernández-Lobato, Kun Zhang, Bernhard Schölkopf:
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation. CoRR abs/2012.09092 (2020) - 2019
- [i129]Arash Mehrjou, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet:
Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces. CoRR abs/1901.09206 (2019) - [i128]Mateo Rojas-Carulla, Ilya O. Tolstikhin, Guillermo Luque, Nicholas D. Youngblut, Ruth E. Ley, Bernhard Schölkopf:
GeNet: Deep Representations for Metagenomics. CoRR abs/1901.11015 (2019) - [i127]Niki Kilbertus, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, Isabel Valera:
Improving Consequential Decision Making under Imperfect Predictions. CoRR abs/1902.02979 (2019) - [i126]Diego Agudelo-España, Sebastián Gómez-González, Stefan Bauer, Bernhard Schölkopf, Jan Peters:
Bayesian Online Detection and Prediction of Change Points. CoRR abs/1902.04524 (2019) - [i125]Philippe Wenk, Gabriele Abbati, Stefan Bauer, Michael A. Osborne, Andreas Krause, Bernhard Schölkopf:
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems. CoRR abs/1902.06278 (2019) - [i124]Gabriele Abbati, Philippe Wenk, Stefan Bauer, Michael A. Osborne, Andreas Krause, Bernhard Schölkopf:
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs. CoRR abs/1902.08480 (2019) - [i123]Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf:
Causal Discovery from Heterogeneous/Nonstationary Data. CoRR abs/1903.01672 (2019) - [i122]Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael J. Black, Bernhard Schölkopf:
From Variational to Deterministic Autoencoders. CoRR abs/1903.12436 (2019) - [i121]Timothy D. Gebhard, Niki Kilbertus, Ian Harry, Bernhard Schölkopf:
Convolutional neural networks: a magic bullet for gravitational-wave detection? CoRR abs/1904.08693 (2019) - [i120]Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem:
Disentangling Factors of Variation Using Few Labels. CoRR abs/1905.01258 (2019) - [i119]Behzad Tabibian, Vicenç Gómez, Abir De, Bernhard Schölkopf, Manuel Gomez Rodriguez:
Consequential Ranking Algorithms and Long-term Welfare. CoRR abs/1905.05305 (2019) - [i118]Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal, Amit Raj, James Hays, Bernhard Schölkopf:
Kernel Mean Matching for Content Addressability of GANs. CoRR abs/1905.05882 (2019) - [i117]Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, Bernhard Schölkopf:
The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA. CoRR abs/1905.06642 (2019) - [i116]Moein Khajehnejad, Behzad Tabibian, Bernhard Schölkopf, Adish Singla, Manuel Gomez-Rodriguez:
Optimal Decision Making Under Strategic Behavior. CoRR abs/1905.09239 (2019) - [i115]Julius von Kügelgen, Marco Loog, Alexander Mey, Bernhard Schölkopf:
Semi-Supervised Learning, Causality and the Conditional Cluster Assumption. CoRR abs/1905.12081 (2019) - [i114]Jonas M. Kübler, Krikamol Muandet, Bernhard Schölkopf:
Quantum Mean Embedding of Probability Distributions. CoRR abs/1905.13526 (2019) - [i113]Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem:
On the Fairness of Disentangled Representations. CoRR abs/1905.13662 (2019) - [i112]Ðorðe Miladinovic, Muhammad Waleed Gondal, Bernhard Schölkopf, Joachim M. Buhmann, Stefan Bauer:
Disentangled State Space Representations. CoRR abs/1906.03255 (2019) - [i111]Muhammad Waleed Gondal, Manuel Wüthrich, Ðorðe Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer:
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. CoRR abs/1906.03292 (2019) - [i110]Sebastián Gómez-González, Yassine Nemmour, Bernhard Schölkopf, Jan Peters:
Reliable Real Time Ball Tracking for Robot Table Tennis. CoRR abs/1908.07332 (2019) - [i109]Sebastián Gómez-González, Sergey Prokudin, Bernhard Schölkopf, Jan Peters:
Real Time Trajectory Prediction Using Deep Conditional Generative Models. CoRR abs/1909.03895 (2019) - [i108]Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, Bernhard Schölkopf:
Recurrent Independent Mechanisms. CoRR abs/1909.10893 (2019) - [i107]Julius von Kügelgen, Paul K. Rubenstein, Bernhard Schölkopf, Adrian Weller:
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks. CoRR abs/1910.03962 (2019) - [i106]Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf, Wittawat Jitkrittum:
Kernel Stein Tests for Multiple Model Comparison. CoRR abs/1910.12252 (2019) - [i105]Bernhard Schölkopf:
Causality for Machine Learning. CoRR abs/1911.10500 (2019) - [i104]Jia-Jie Zhu, Krikamol Muandet, Moritz Diehl, Bernhard Schölkopf:
A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming. CoRR abs/1911.11082 (2019) - 2018
- [i103]Carl-Johann Simon-Gabriel, Yann Ollivier, Bernhard Schölkopf, Léon Bottou, David Lopez-Paz:
Adversarial Vulnerability of Neural Networks Increases With Input Dimension. CoRR abs/1802.01421 (2018) - [i102]Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin:
On the Latent Space of Wasserstein Auto-Encoders. CoRR abs/1802.03761 (2018) - [i101]Mehdi S. M. Sajjadi, Bernhard Schölkopf:
Tempered Adversarial Networks. CoRR abs/1802.04374 (2018) - [i100]Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf:
Analysis of Cause-Effect Inference via Regression Errors. CoRR abs/1802.06698 (2018) - [i99]Dominik Janzing, Bernhard Schölkopf:
Detecting non-causal artifacts in multivariate linear regression models. CoRR abs/1803.00810 (2018) - [i98]Rohit Babbar, Bernhard Schölkopf:
Adversarial Extreme Multi-label Classification. CoRR abs/1803.01570 (2018) - [i97]Philipp Geiger, Justus Winkelmann, Claudius Proissl, Michel Besserve, Bernhard Schölkopf:
Coordination via predictive assistants from a game-theoretic view. CoRR abs/1803.06247 (2018) - [i96]Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi:
Revisiting First-Order Convex Optimization Over Linear Spaces. CoRR abs/1803.09539 (2018) - [i95]Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf:
Clustering Meets Implicit Generative Models. CoRR abs/1804.11130 (2018) - [i94]Matthias Bauer, Valentin Volchkov, Michael Hirsch, Bernhard Schölkopf:
Automatic Estimation of Modulation Transfer Functions. CoRR abs/1805.01872 (2018) - [i93]Saeed Saremi, Arash Mehrjou, Bernhard Schölkopf, Aapo Hyvärinen:
Deep Energy Estimator Networks. CoRR abs/1805.08306 (2018) - [i92]Friedrich Solowjow, Arash Mehrjou, Bernhard Schölkopf, Sebastian Trimpe:
Minimum Information Exchange in Distributed Systems. CoRR abs/1805.09714 (2018) - [i91]Arash Mehrjou, Friedrich Solowjow, Sebastian Trimpe, Bernhard Schölkopf:
A Local Information Criterion for Dynamical Systems. CoRR abs/1805.10615 (2018) - [i90]Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf:
groupICA: Independent component analysis for grouped data. CoRR abs/1806.01094 (2018) - [i89]Eduardo Pérez-Pellitero, Mehdi S. M. Sajjadi, Michael Hirsch, Bernhard Schölkopf:
Photorealistic Video Super Resolution. CoRR abs/1807.07930 (2018) - [i88]Muhammad Waleed Gondal, Bernhard Schölkopf, Michael Hirsch:
The Unreasonable Effectiveness of Texture Transfer for Single Image Super-resolution. CoRR abs/1808.00043 (2018) - [i87]Alexander Neitz, Giambattista Parascandolo, Stefan Bauer, Bernhard Schölkopf:
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models. CoRR abs/1808.04768 (2018) - [i86]Sebastián Gómez-González, Gerhard Neumann, Bernhard Schölkopf, Jan Peters:
Adaptation and Robust Learning of Probabilistic Movement Primitives. CoRR abs/1808.10648 (2018) - [i85]Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton:
Informative Features for Model Comparison. CoRR abs/1810.11630 (2018) - [i84]Raphael Suter, Ðorðe Miladinovic, Stefan Bauer, Bernhard Schölkopf:
Interventional Robustness of Deep Latent Variable Models. CoRR abs/1811.00007 (2018) - [i83]Arash Mehrjou, Bernhard Schölkopf:
Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems. CoRR abs/1811.05933 (2018) - [i82]Francesco Locatello, Stefan Bauer, Mario Lucic, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. CoRR abs/1811.12359 (2018) - [i81]Niki Kilbertus, Giambattista Parascandolo, Bernhard Schölkopf:
Generalization in anti-causal learning. CoRR abs/1812.00524 (2018) - [i80]Michel Besserve, Rémy Sun, Bernhard Schölkopf:
Counterfactuals uncover the modular structure of deep generative models. CoRR abs/1812.03253 (2018) - [i79]Chaochao Lu, Bernhard Schölkopf, José Miguel Hernández-Lobato:
Deconfounding Reinforcement Learning in Observational Settings. CoRR abs/1812.10576 (2018) - 2017
- [i78]Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf:
AdaGAN: Boosting Generative Models. CoRR abs/1701.02386 (2017) - [i77]Lei Xiao, Felix Heide, Wolfgang Heidrich, Bernhard Schölkopf, Michael Hirsch:
Discriminative Transfer Learning for General Image Restoration. CoRR abs/1703.09245 (2017) - [i76]Tae Hyun Kim, Kyoung Mu Lee, Bernhard Schölkopf, Michael Hirsch:
Online Video Deblurring via Dynamic Temporal Blending Network. CoRR abs/1704.03285 (2017) - [i75]Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing:
Group invariance principles for causal generative models. CoRR abs/1705.02212 (2017) - [i74]Anastasia-Atalanti Mastakouri, Sebastian Weichwald, Ozan Özdenizci, Timm Meyer, Bernhard Schölkopf, Moritz Grosse-Wentrup:
Personalized Brain-Computer Interface Models for Motor Rehabilitation. CoRR abs/1705.03259 (2017) - [i73]Arash Mehrjou, Bernhard Schölkopf, Saeed Saremi:
Annealed Generative Adversarial Networks. CoRR abs/1705.07505 (2017) - [i72]Matthias Bauer, Mateo Rojas-Carulla, Jakub Bartlomiej Swiatkowski, Bernhard Schölkopf, Richard E. Turner:
Discriminative k-shot learning using probabilistic models. CoRR abs/1706.00326 (2017) - [i71]Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Bernhard Schölkopf, Sergey Levine:
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. CoRR abs/1706.00387 (2017) - [i70]Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf:
Avoiding Discrimination through Causal Reasoning. CoRR abs/1706.02744 (2017) - [i69]Paul K. Rubenstein, Ilya O. Tolstikhin, Philipp Hennig, Bernhard Schölkopf:
Probabilistic Active Learning of Functions in Structural Causal Models. CoRR abs/1706.10234 (2017) - [i68]Paul K. Rubenstein, Sebastian Weichwald, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf:
Causal Consistency of Structural Equation Models. CoRR abs/1707.00819 (2017) - [i67]Patrick Wieschollek, Michael Hirsch, Bernhard Schölkopf, Hendrik P. A. Lensch:
Learning Blind Motion Deblurring. CoRR abs/1708.04208 (2017) - [i66]Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf:
Wasserstein Auto-Encoders. CoRR abs/1711.01558 (2017) - [i65]Mostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schölkopf:
Fidelity-Weighted Learning. CoRR abs/1711.02799 (2017) - [i64]Jooyeon Kim, Behzad Tabibian, Alice Oh, Bernhard Schölkopf, Manuel Gomez-Rodriguez:
Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation. CoRR abs/1711.09918 (2017) - [i63]Giambattista Parascandolo, Mateo Rojas-Carulla, Niki Kilbertus, Bernhard Schölkopf:
Learning Independent Causal Mechanisms. CoRR abs/1712.00961 (2017) - [i62]Behzad Tabibian, Utkarsh Upadhyay, Abir De, Ali Zarezade, Bernhard Schölkopf, Manuel Gomez-Rodriguez:
Optimizing Human Learning. CoRR abs/1712.01856 (2017) - 2016
- [i61]Philipp Geiger, Lucian Carata, Bernhard Schölkopf:
Causal models for debugging and control in cloud computing. CoRR abs/1603.01581 (2016) - [i60]Sebastian Weichwald, Arthur Gretton, Bernhard Schölkopf, Moritz Grosse-Wentrup:
Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data. CoRR abs/1605.00391 (2016) - [i59]Sebastian Weichwald, Tatiana Fomina, Bernhard Schölkopf, Moritz Grosse-Wentrup:
Optimal Coding in Biological and Artificial Neural Networks. CoRR abs/1605.07094 (2016) - [i58]David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Schölkopf, Léon Bottou:
Discovering Causal Signals in Images. CoRR abs/1605.08179 (2016) - [i57]Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Bernhard Schölkopf:
Kernel Mean Embedding of Distributions: A Review and Beyonds. CoRR abs/1605.09522 (2016) - [i56]Philipp Geiger, Katja Hofmann, Bernhard Schölkopf:
Experimental and causal view on information integration in autonomous agents. CoRR abs/1606.04250 (2016) - [i55]Patrick Wieschollek, Michael Hirsch, Hendrik P. A. Lensch, Bernhard Schölkopf:
End-to-End Learning for Image Burst Deblurring. CoRR abs/1607.04433 (2016) - [i54]Paul K. Rubenstein, Stephan Bongers, Joris M. Mooij, Bernhard Schölkopf:
From Deterministic ODEs to Dynamic Structural Causal Models. CoRR abs/1608.08028 (2016) - [i53]Mehdi S. M. Sajjadi, Rolf Köhler, Bernhard Schölkopf, Michael Hirsch:
Depth Estimation Through a Generative Model of Light Field Synthesis. CoRR abs/1609.01499 (2016) - [i52]Rohit Babbar, Bernhard Schölkopf:
DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification. CoRR abs/1609.02521 (2016) - [i51]Anant Raj, Jakob Olbrich, Bernd Gärtner, Bernhard Schölkopf, Martin Jaggi:
Screening Rules for Convex Problems. CoRR abs/1609.07478 (2016) - [i50]Behzad Tabibian, Isabel Valera, Mehrdad Farajtabar, Le Song, Bernhard Schölkopf, Manuel Gomez-Rodriguez:
Distilling Information Reliability and Source Trustworthiness from Digital Traces. CoRR abs/1610.07472 (2016) - [i49]Stephan Bongers, Jonas Peters, Bernhard Schölkopf, Joris M. Mooij:
Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions. CoRR abs/1611.06221 (2016) - [i48]Anant Raj, Abhishek Kumar, Youssef Mroueh, P. Thomas Fletcher, Bernhard Schölkopf:
Local Group Invariant Representations via Orbit Embeddings. CoRR abs/1612.01988 (2016) - [i47]Mehdi S. M. Sajjadi, Bernhard Schölkopf, Michael Hirsch:
EnhanceNet: Single Image Super-Resolution through Automated Texture Synthesis. CoRR abs/1612.07919 (2016) - [i46]Arthur Gretton, Philipp Hennig, Carl Edward Rasmussen, Bernhard Schölkopf:
New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481). Dagstuhl Reports 6(11): 142-167 (2016) - 2015
- [i45]Bernhard Schölkopf, Krikamol Muandet, Kenji Fukumizu, Jonas Peters:
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations. CoRR abs/1501.06794 (2015) - [i44]Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf, Michel Besserve:
Telling cause from effect in deterministic linear dynamical systems. CoRR abs/1503.01299 (2015) - [i43]Kun Zhang, Jiji Zhang, Bernhard Schölkopf:
Distinguishing Cause from Effect Based on Exogeneity. CoRR abs/1504.05651 (2015) - [i42]Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters:
Removing systematic errors for exoplanet search via latent causes. CoRR abs/1505.03036 (2015) - [i41]Kun Zhang, Biwei Huang, Bernhard Schölkopf, Michel Besserve, Masataka Watanabe, Dajiang Zhu:
Towards Robust and Specific Causal Discovery from fMRI. CoRR abs/1509.08056 (2015) - [i40]Sebastian Weichwald, Timm Meyer, Ozan Özdenizci, Bernhard Schölkopf, Tonio Ball, Moritz Grosse-Wentrup:
Causal interpretation rules for encoding and decoding models in neuroimaging. CoRR abs/1511.04780 (2015) - [i39]Vinay Jayaram, Morteza Alamgir, Yasemin Altun, Bernhard Schölkopf, Moritz Grosse-Wentrup:
Transfer Learning in Brain-Computer Interfaces. CoRR abs/1512.00296 (2015) - [i38]Sebastian Weichwald, Bernhard Schölkopf, Tonio Ball, Moritz Grosse-Wentrup:
Causal and anti-causal learning in pattern recognition for neuroimaging. CoRR abs/1512.04808 (2015) - 2014
- [i37]David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf:
Randomized Nonlinear Component Analysis. CoRR abs/1402.0119 (2014) - [i36]Manuel Gomez-Rodriguez, Krishna P. Gummadi, Bernhard Schölkopf:
Quantifying Information Overload in Social Media and its Impact on Social Contagions. CoRR abs/1403.6838 (2014) - [i35]Hadi Daneshmand, Manuel Gomez-Rodriguez, Le Song, Bernhard Schölkopf:
Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm. CoRR abs/1405.2936 (2014) - [i34]Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Shrinkage Estimators. CoRR abs/1405.5505 (2014) - [i33]Dustin Lang, David W. Hogg, Bernhard Schölkopf:
Towards building a Crowd-Sourced Sky Map. CoRR abs/1406.1528 (2014) - [i32]Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf:
Learning to Deblur. CoRR abs/1406.7444 (2014) - [i31]Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
From Ordinary Differential Equations to Structural Causal Models: the deterministic case. CoRR abs/1408.2063 (2014) - [i30]Krikamol Muandet, Bernhard Schölkopf:
One-Class Support Measure Machines for Group Anomaly Detection. CoRR abs/1408.2064 (2014) - [i29]Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf:
Distinguishing cause from effect using observational data: methods and benchmarks. CoRR abs/1412.3773 (2014) - 2013
- [i28]David Lopez-Paz, José Miguel Hernández-Lobato, Bernhard Schölkopf:
Semi-Supervised Domain Adaptation with Non-Parametric Copulas. CoRR abs/1301.0142 (2013) - [i27]Krikamol Muandet, David Balduzzi, Bernhard Schölkopf:
Domain Generalization via Invariant Feature Representation. CoRR abs/1301.2115 (2013) - [i26]Krikamol Muandet, Bernhard Schölkopf:
One-Class Support Measure Machines for Group Anomaly Detection. CoRR abs/1303.0309 (2013) - [i25]Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
From Ordinary Differential Equations to Structural Causal Models: the deterministic case. CoRR abs/1304.7920 (2013) - [i24]Manuel Gomez-Rodriguez, Jure Leskovec, Bernhard Schölkopf:
Modeling Information Propagation with Survival Theory. CoRR abs/1305.3616 (2013) - [i23]Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf:
Kernel Mean Estimation and Stein's Effect. CoRR abs/1306.0842 (2013) - [i22]Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schölkopf:
Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. CoRR abs/1309.6860 (2013) - [i21]Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schölkopf:
Consistency of Causal Inference under the Additive Noise Model. CoRR abs/1312.5770 (2013) - 2012
- [i20]Dominik Janzing, Eleni Sgouritsa, Oliver Stegle, Jonas Peters, Bernhard Schölkopf:
Detecting low-complexity unobserved causes. CoRR abs/1202.3737 (2012) - [i19]Jonas Peters, Joris M. Mooij, Dominik Janzing, Bernhard Schölkopf:
Identifiability of Causal Graphs using Functional Models. CoRR abs/1202.3757 (2012) - [i18]Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf:
Kernel-based Conditional Independence Test and Application in Causal Discovery. CoRR abs/1202.3775 (2012) - [i17]Krikamol Muandet, Bernhard Schölkopf, Kenji Fukumizu, Francesco Dinuzzo:
Learning from Distributions via Support Measure Machines. CoRR abs/1202.6504 (2012) - [i16]Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf:
Inferring deterministic causal relations. CoRR abs/1203.3475 (2012) - [i15]Kun Zhang, Bernhard Schölkopf, Dominik Janzing:
Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery. CoRR abs/1203.3534 (2012) - [i14]Manuel Gomez-Rodriguez, Bernhard Schölkopf:
Submodular Inference of Diffusion Networks from Multiple Trees. CoRR abs/1205.1671 (2012) - [i13]Manuel Gomez-Rodriguez, Bernhard Schölkopf:
Influence Maximization in Continuous Time Diffusion Networks. CoRR abs/1205.1682 (2012) - [i12]Francesco Dinuzzo, Bernhard Schölkopf:
The representer theorem for Hilbert spaces: a necessary and sufficient condition. CoRR abs/1205.1928 (2012) - [i11]Dominik Janzing, Jonas Peters, Joris M. Mooij, Bernhard Schölkopf:
Identifying confounders using additive noise models. CoRR abs/1205.2640 (2012) - [i10]Jonas Peters, Dominik Janzing, Bernhard Schölkopf:
Causal Inference on Time Series using Structural Equation Models. CoRR abs/1207.5136 (2012) - [i9]Manuel Gomez-Rodriguez, Jure Leskovec, Bernhard Schölkopf:
Structure and Dynamics of Information Pathways in Online Media. CoRR abs/1212.1464 (2012) - [i8]Dominik G. Grimm, Bastian Greshake, Stefan Kleeberger, Christoph Lippert, Oliver Stegle, Bernhard Schölkopf, Detlef Weigel, Karsten M. Borgwardt:
easyGWAS: An integrated interspecies platform for performing genome-wide association studies. CoRR abs/1212.4788 (2012) - 2011
- [i7]Manuel Gomez-Rodriguez, David Balduzzi, Bernhard Schölkopf:
Uncovering the Temporal Dynamics of Diffusion Networks. CoRR abs/1105.0697 (2011) - [i6]Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Kun Zhang:
Robust Learning via Cause-Effect Models. CoRR abs/1112.2738 (2011) - 2010
- [i5]Bastian Steudel, Dominik Janzing, Bernhard Schölkopf:
Causal Markov condition for submodular information measures. CoRR abs/1002.4020 (2010) - 2009
- [i4]Dominik Janzing, Steffen L. Lauritzen, Bernhard Schölkopf:
09401 Abstracts Collection - Machine learning approaches to statistical dependences and causality. Machine learning approaches to statistical dependences and causality 2009 - [i3]Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Gert R. G. Lanckriet, Bernhard Schölkopf:
A note on integral probability metrics and $\phi$-divergences. CoRR abs/0901.2698 (2009) - 2008
- [i2]Dominik Janzing, Bernhard Schölkopf:
Causal inference using the algorithmic Markov condition. CoRR abs/0804.3678 (2008) - [i1]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Method for the Two-Sample Problem. CoRR abs/0805.2368 (2008)
Coauthor Index
[j115] [j113] [j111] [j107] [j103] [j98] [j95] [c413] [c412] [c401] [c361] [c347] [c342] [c330] [c328] [c323] [c321] [c317] [c313] [c312] [c303] [c302] [c299] [c296] [c293] [c284] [c279] [c278] [c277] [c275] [c273] [c268] [c267] [c244] [c217] [i350] [i284] [i283] [i263] [i262] [i258] [i221] [i212] [i207] [i194] [i190] [i188] [i186] [i183] [i165] [i154] [i153] [i151] [i150] [i149] [i146] [i144] [i140] [i137] [i134] [i133] [i126] [i125] [i124] [i120] [i113] [i112] [i111] [i87] [i84] [i82]
Dieter Büchler
aka: Dieter Buchler
aka: Dieter Buchler
Peter V. Gehler
aka: Peter Vincent Gehler
aka: Peter Vincent Gehler
Manuel Gomez-Rodriguez
aka: Manuel Gomez Rodriguez
aka: Manuel Gomez Rodriguez
[j92] [j82] [j79] [j77] [j72] [j67] [j61] [j55] [j50] [j45] [c374] [c364] [c357] [c354] [c337] [c320] [c270] [c269] [c263] [c262] [c250] [c230] [c225] [c208] [c203] [c200] [c199] [c188] [c182] [c180] [c177] [c168] [c165] [c163] [c156] [c150] [c141] [c139] [c138] [c137] [c136] [c133] [c127] [c124] [c123] [c122] [c121] [c118] [c117] [c112] [c107] [c102] [c93] [c92] [c84] [e7] [e6] [i261] [i257] [i182] [i163] [i100] [i99] [i75] [i70] [i68] [i44] [i42] [i31] [i29] [i25] [i22] [i21] [i20] [i19] [i18] [i16] [i15] [i11] [i10] [i6] [i5] [i4] [i2]
Julius von Kügelgen
aka: Bodo Julius von Kügelgen
aka: Bodo Julius von Kügelgen
[j122] [j118] [j114] [j102] [c421] [c414] [c408] [c394] [c390] [c389] [c383] [c382] [c364] [c363] [c357] [c350] [c344] [c343] [c332] [c314] [c308] [c304] [c295] [c290] [c287] [d3] [d2] [i302] [i297] [i295] [i290] [i285] [i277] [i269] [i264] [i261] [i260] [i253] [i247] [i246] [i239] [i232] [i218] [i201] [i200] [i197] [i192] [i189] [i184] [i182] [i179] [i161] [i156] [i138] [i115] [i107]
[j103] [j98] [c421] [c419] [c409] [c393] [c372] [c364] [c363] [c361] [c359] [c354] [c341] [c340] [c328] [c317] [c314] [c310] [c308] [c302] [c296] [c294] [c279] [c275] [c268] [c267] [c264] [c256] [c249] [i318] [i307] [i262] [i257] [i256] [i240] [i233] [i228] [i222] [i212] [i201] [i193] [i192] [i190] [i189] [i182] [i180] [i179] [i169] [i153] [i149] [i134] [i133] [i120] [i117] [i113] [i111] [i96] [i95] [i82]
David Lopez-Paz
aka: David López-Paz
aka: David López-Paz
Atalanti-Anastasia Mastakouri
aka: Anastasia-Atalanti Mastakouri
aka: Anastasia-Atalanti Mastakouri
Michael Muehlebach
aka: Michael Mühlebach
aka: Michael Mühlebach
Alexander J. Smola
aka: Alexander Johannes Smola
aka: Alexander Johannes Smola
[b2] [j60] [j42] [j30] [j29] [j25] [j24] [j19] [j15] [j14] [j13] [j10] [j9] [j8] [j7] [j4] [j3] [c186] [c106] [c98] [c97] [c94] [c89] [c83] [c81] [c80] [c75] [c74] [c42] [c41] [c37] [c35] [c29] [c28] [c27] [c23] [c22] [c21] [c20] [c19] [c18] [c17] [c16] [c15] [c13] [c12] [c11] [c9] [c7] [c6] [c5] [i147] [i37] [i1]
Jonas Wildberger
aka: Jonas Bernhard Wildberger
aka: Jonas Bernhard Wildberger
Manuel Wüthrich
aka: Manuel Wuthrich
aka: Manuel Wuthrich
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