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ORCIDBernd Bischl
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- affiliation: LMU Munich, Department of Statistics, Germany
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2020 – today
- 2023
[j38]Daniel Schalk
, Verena S. Hoffmann, Bernd Bischl, Ulrich Mansmann:
dsBinVal: Conducting distributed ROC analysis using DataSHIELD. J. Open Source Softw. 8(83): 4545 (2023)- [j37]David Rügamer, Chris Kolb, Cornelius Fritz, Florian Pfisterer, Philipp Kopper, Bernd Bischl, Ruolin Shen, Christina Bukas, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, Philipp F. M. Baumann, Lucas Kook, Nadja Klein, Christian L. Müller:
deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. J. Stat. Softw. 105(2) (2023) - [j36]Bernd Bischl, Martin Binder, Michel Lang, Tobias Pielok, Jakob Richter, Stefan Coors, Janek Thomas, Theresa Ullmann, Marc Becker, Anne-Laure Boulesteix, Difan Deng, Marius Lindauer:
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. WIREs Data. Mining. Knowl. Discov. 13(2) (2023) - [c69]Matthias Feurer, Katharina Eggensperger, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter:
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. IDA 2023: 130-142 - [i88]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition. CoRR abs/2301.06293 (2023) - [i87]Hilde J. P. Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor H. Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter:
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML. CoRR abs/2303.08485 (2023) - [i86]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis. CoRR abs/2303.11224 (2023) - [i85]Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer:
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. CoRR abs/2304.02902 (2023) - [i84]Susanne Dandl, Andreas Hofheinz, Martin Binder, Bernd Bischl, Giuseppe Casalicchio:
counterfactuals: An R Package for Counterfactual Explanation Methods. CoRR abs/2304.06569 (2023) - [i83]Felix Ott, Lucas Heublein, David Rügamer, Bernd Bischl, Christopher Mutschler:
Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments. CoRR abs/2304.07250 (2023) - [i82]Susanne Dandl, Giuseppe Casalicchio, Bernd Bischl, Ludwig Bothmann:
Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. CoRR abs/2305.02780 (2023) - 2022
- [j35]Florian Pargent, Florian Pfisterer, Janek Thomas, Bernd Bischl:
Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. Comput. Stat. 37(5): 2671-2692 (2022) - [j34]Quay Au, Julia Herbinger, Clemens Stachl, Bernd Bischl, Giuseppe Casalicchio:
Grouped feature importance and combined features effect plot. Data Min. Knowl. Discov. 36(4): 1401-1450 (2022) - [j33]Felix Ott, David Rügamer, Lucas Heublein, Tim Hamann, Jens Barth, Bernd Bischl, Christopher Mutschler:
Benchmarking online sequence-to-sequence and character-based handwriting recognition from IMU-enhanced pens. Int. J. Document Anal. Recognit. 25(4): 385-414 (2022) - [j32]Julia Moosbauer, Martin Binder, Lennart Schneider, Florian Pfisterer, Marc Becker, Michel Lang, Lars Kotthoff, Bernd Bischl:
Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers. IEEE Trans. Evol. Comput. 26(6): 1336-1350 (2022) - [c68]Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio:
REPID: Regional Effect Plots with implicit Interaction Detection. AISTATS 2022: 10209-10233 - [c67]Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl:
YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization. AutoML 2022: 3/1-39 - [c66]Lennart Schneider, Florian Pfisterer, Paul Kent, Jürgen Branke, Bernd Bischl, Janek Thomas:
Tackling Neural Architecture Search With Quality Diversity Optimization. AutoML 2022: 9/1-30 - [c65]Susanne Dandl, Florian Pfisterer, Bernd Bischl:
Multi-objective counterfactual fairness. GECCO Companion 2022: 328-331 - [c64]Lennart Schneider, Florian Pfisterer, Janek Thomas, Bernd Bischl:
A collection of quality diversity optimization problems derived from hyperparameter optimization of machine learning models. GECCO Companion 2022: 2136-2142 - [c63]Mina Rezaei, Emilio Dorigatti, David Rügamer, Bernd Bischl:
Joint Debiased Representation Learning and Imbalanced Data Clustering. ICDM (Workshops) 2022: 55-62 - [c62]Andreas Klaß, Sven M. Lorenz, Martin W. Lauer-Schmaltz, David Rügamer, Bernd Bischl, Christopher Mutschler, Felix Ott:
Uncertainty-aware Evaluation of Time-series Classification for Online Handwriting Recognition with Domain Shift. STRL@IJCAI 2022 - [c61]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs. MAD@MICCAI 2022: 22-32 - [c60]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift. ACM Multimedia 2022: 5934-5943 - [c59]Mehmet Ozgur Turkoglu, Alexander Becker, Hüseyin Anil Gündüz, Mina Rezaei, Bernd Bischl, Rodrigo Caye Daudt, Stefano D'Aronco, Jan D. Wegner, Konrad Schindler:
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation. NeurIPS 2022 - [c58]Philipp Kopper, Simon Wiegrebe, Bernd Bischl, Andreas Bender, David Rügamer:
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis. PAKDD (2) 2022: 249-261 - [c57]David Rügamer, Andreas Bender, Simon Wiegrebe, Daniel Racek, Bernd Bischl, Christian L. Müller, Clemens Stachl:
Factorized Structured Regression for Large-Scale Varying Coefficient Models. ECML/PKDD (5) 2022: 20-35 - [c56]Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer:
Efficient Automated Deep Learning for Time Series Forecasting. ECML/PKDD (3) 2022: 664-680 - [c55]Lennart Schneider, Lennart Schäpermeier, Raphael Patrick Prager, Bernd Bischl, Heike Trautmann, Pascal Kerschke:
HPO ˟ ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. PPSN (1) 2022: 575-589 - [c54]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Joint Classification and Trajectory Regression of Online Handwriting using a Multi-Task Learning Approach. WACV 2022: 1244-1254 - [i81]Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl, Christian Heumann:
Marginal Effects for Non-Linear Prediction Functions. CoRR abs/2201.08837 (2022) - [i80]Emilio Dorigatti, Jann Goschenhofer, Benjamin Schubert, Mina Rezaei, Bernd Bischl:
Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection. CoRR abs/2201.13192 (2022) - [i79]Felix Ott, David Rügamer, Lucas Heublein, Tim Hamann, Jens Barth, Bernd Bischl, Christopher Mutschler:
Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens. CoRR abs/2202.07036 (2022) - [i78]Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio:
REPID: Regional Effect Plots with implicit Interaction Detection. CoRR abs/2202.07254 (2022) - [i77]Philipp Kopper, Simon Wiegrebe, Bernd Bischl, Andreas Bender, David Rügamer:
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis. CoRR abs/2202.07423 (2022) - [i76]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Cross-Modal Common Representation Learning with Triplet Loss Functions. CoRR abs/2202.07901 (2022) - [i75]Daniel Schalk, Verena S. Hoffmann, Bernd Bischl, Ulrich Mansmann:
Distributed non-disclosive validation of predictive models by a modified ROC-GLM. CoRR abs/2203.10828 (2022) - [i74]Ashkan Khakzar, Yawei Li, Yang Zhang, Mirac Sanisoglu, Seong Tae Kim, Mina Rezaei, Bernd Bischl, Nassir Navab:
Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models. CoRR abs/2204.01729 (2022) - [i73]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift. CoRR abs/2204.03342 (2022) - [i72]Lennart Schneider, Florian Pfisterer, Janek Thomas, Bernd Bischl:
A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models. CoRR abs/2204.14061 (2022) - [i71]Difan Deng, Florian Karl, Frank Hutter, Bernd Bischl, Marius Lindauer:
Efficient Automated Deep Learning for Time Series Forecasting. CoRR abs/2205.05511 (2022) - [i70]Ludwig Bothmann, Kristina Peters, Bernd Bischl:
What Is Fairness? Implications For FairML. CoRR abs/2205.09622 (2022) - [i69]David Rügamer, Andreas Bender, Simon Wiegrebe, Daniel Racek, Bernd Bischl, Christian L. Müller, Clemens Stachl:
Factorized Structured Regression for Large-Scale Varying Coefficient Models. CoRR abs/2205.13080 (2022) - [i68]Mehmet Ozgur Turkoglu, Alexander Becker, Hüseyin Anil Gündüz, Mina Rezaei, Bernd Bischl, Rodrigo Caye Daudt, Stefano D'Aronco, Jan Dirk Wegner, Konrad Schindler:
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation. CoRR abs/2206.00050 (2022) - [i67]Julia Moosbauer, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl:
Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. CoRR abs/2206.05447 (2022) - [i66]Florian Karl, Tobias Pielok, Julia Moosbauer, Florian Pfisterer, Stefan Coors, Martin Binder, Lennart Schneider, Janek Thomas, Jakob Richter, Michel Lang, Eduardo C. Garrido-Merchán, Jürgen Branke, Bernd Bischl:
Multi-Objective Hyperparameter Optimization - An Overview. CoRR abs/2206.07438 (2022) - [i65]Andreas Klaß, Sven M. Lorenz, Martin W. Lauer-Schmaltz, David Rügamer, Bernd Bischl, Christopher Mutschler, Felix Ott:
Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift. CoRR abs/2206.08640 (2022) - [i64]Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren:
AMLB: an AutoML Benchmark. CoRR abs/2207.12560 (2022) - [i63]Lennart Schneider, Florian Pfisterer, Paul Kent, Jürgen Branke, Bernd Bischl, Janek Thomas:
Tackling Neural Architecture Search With Quality Diversity Optimization. CoRR abs/2208.00204 (2022) - [i62]Lennart Schneider, Lennart Schäpermeier, Raphael Patrick Prager, Bernd Bischl, Heike Trautmann, Pascal Kerschke:
HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. CoRR abs/2208.00220 (2022) - [i61]Felix Ott, Nisha Lakshmana Raichur, David Rügamer, Tobias Feigl, Heiko Neumann, Bernd Bischl, Christopher Mutschler:
Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression. CoRR abs/2208.00919 (2022) - [i60]Emilio Dorigatti, Jonas Schweisthal, Bernd Bischl, Mina Rezaei:
Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision. CoRR abs/2209.02459 (2022) - [i59]Shunjie-Fabian Zheng, JaeEun Nam, Emilio Dorigatti, Bernd Bischl, Shekoofeh Azizi, Mina Rezaei:
Joint Debiased Representation and Image Clustering Learning with Self-Supervision. CoRR abs/2209.06941 (2022) - [i58]Emilio Dorigatti, Bernd Bischl, Benjamin Schubert:
Improved proteasomal cleavage prediction with positive-unlabeled learning. CoRR abs/2209.07527 (2022) - [i57]Daniel Schalk, Bernd Bischl, David Rügamer:
Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models. CoRR abs/2210.07723 (2022) - [i56]Matthias Feurer, Katharina Eggensperger, Edward Bergman, Florian Pfisterer, Bernd Bischl, Frank Hutter:
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives. CoRR abs/2212.04183 (2022) - 2021
- [j31]Ilias Gerostathopoulos, Frantisek Plásil, Christian Prehofer, Janek Thomas, Bernd Bischl:
Automated Online Experiment-Driven Adaptation-Mechanics and Cost Aspects. IEEE Access 9: 58079-58087 (2021) - [j30]Raphael Sonabend, Franz J. Király, Andreas Bender, Bernd Bischl, Michel Lang:
mlr3proba: an R package for machine learning in survival analysis. Bioinform. 37(17): 2789-2791 (2021) - [j29]Nicole Ellenbach, Anne-Laure Boulesteix, Bernd Bischl, Kristian Unger, Roman Hornung:
Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning. J. Classif. 38(2): 212-231 (2021) - [j28]Martin Binder, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, Bernd Bischl:
mlr3pipelines - Flexible Machine Learning Pipelines in R. J. Mach. Learn. Res. 22: 184:1-184:7 (2021) - [j27]Florian Pfisterer, Christoph Kern, Susanne Dandl, Matthew Sun, Michael Kim, Bernd Bischl:
mcboost: Multi-Calibration Boosting for R. J. Open Source Softw. 6(64): 3453 (2021) - [j26]Patrick Schratz, Jannes Muenchow, Eugenia Iturritxa, José Cortés, Bernd Bischl, Alexander Brenning:
Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques? Remote. Sens. 13(23): 4832 (2021) - [c53]Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren:
Meta-learning for symbolic hyperparameter defaults. GECCO Companion 2021: 151-152 - [c52]Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas C. Müller, Bernd Bischl:
Learning multiple defaults for machine learning algorithms. GECCO Companion 2021: 241-242 - [c51]Jann Goschenhofer, Rasmus Hvingelby, David Rügamer, Janek Thomas, Moritz Wagner, Bernd Bischl:
Deep Semi-supervised Learning for Time Series Classification. ICMLA 2021: 422-428 - [c50]Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren:
OpenML Benchmarking Suites. NeurIPS Datasets and Benchmarks 2021 - [c49]Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl:
Explaining Hyperparameter Optimization via Partial Dependence Plots. NeurIPS 2021: 2280-2291 - [c48]Philipp Kopper, Sebastian Pölsterl, Christian Wachinger, Bernd Bischl, Andreas Bender, David Rügamer:
Semi-Structured Deep Piecewise Exponential Models. SPACA 2021: 40-53 - [i55]Jann Goschenhofer, Rasmus Hvingelby, David Rügamer, Janek Thomas, Moritz Wagner, Bernd Bischl:
Deep Semi-Supervised Learning for Time Series Classification. CoRR abs/2102.03622 (2021) - [i54]Florian Pargent, Florian Pfisterer, Janek Thomas, Bernd Bischl:
Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. CoRR abs/2104.00629 (2021) - [i53]David Rügamer, Ruolin Shen, Christina Bukas, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, Nadja Klein, Chris Kolb, Florian Pfisterer, Philipp Kopper, Bernd Bischl, Christian L. Müller:
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. CoRR abs/2104.02705 (2021) - [i52]Quay Au, Julia Herbinger, Clemens Stachl, Bernd Bischl, Giuseppe Casalicchio:
Grouped Feature Importance and Combined Features Effect Plot. CoRR abs/2104.11688 (2021) - [i51]Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren:
Meta-Learning for Symbolic Hyperparameter Defaults. CoRR abs/2106.05767 (2021) - [i50]Gunnar König, Timo Freiesleben, Bernd Bischl, Giuseppe Casalicchio, Moritz Grosse-Wentrup:
Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT). CoRR abs/2106.08086 (2021) - [i49]Bernd Bischl, Martin Binder, Michel Lang, Tobias Pielok, Jakob Richter, Stefan Coors, Janek Thomas, Theresa Ullmann, Marc Becker, Anne-Laure Boulesteix, Difan Deng, Marius Lindauer:
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. CoRR abs/2107.05847 (2021) - [i48]Lennart Schneider, Florian Pfisterer, Martin Binder, Bernd Bischl:
Mutation is all you need. CoRR abs/2107.07343 (2021) - [i47]Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl:
Developing Open Source Educational Resources for Machine Learning and Data Science. CoRR abs/2107.14330 (2021) - [i46]Christoph Molnar, Timo Freiesleben, Gunnar König, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl:
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. CoRR abs/2109.01433 (2021) - [i45]Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, Bernd Bischl:
YAHPO Gym - Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization. CoRR abs/2109.03670 (2021) - [i44]Mina Rezaei, Emilio Dorigatti, David Rügamer, Bernd Bischl:
Learning Statistical Representation with Joint Deep Embedded Clustering. CoRR abs/2109.05232 (2021) - [i43]Stefan Coors, Daniel Schalk, Bernd Bischl, David Rügamer:
Automatic Componentwise Boosting: An Interpretable AutoML System. CoRR abs/2109.05583 (2021) - [i42]Mina Rezaei, Farzin Soleymani, Bernd Bischl, Shekoofeh Azizi:
Deep Bregman Divergence for Contrastive Learning of Visual Representations. CoRR abs/2109.07455 (2021) - [i41]Farzin Soleymani, Mohammad Eslami, Tobias Elze, Bernd Bischl, Mina Rezaei:
Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Images. CoRR abs/2109.10777 (2021) - [i40]Daniel Schalk, Bernd Bischl, David Rügamer:
Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization. CoRR abs/2110.03513 (2021) - [i39]Tobias Weber, Michael Ingrisch, Matthias Fabritius, Bernd Bischl, David Rügamer:
Survival-oriented embeddings for improving accessibility to complex data structures. CoRR abs/2110.11303 (2021) - [i38]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation. CoRR abs/2110.11312 (2021) - [i37]Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl:
Explaining Hyperparameter Optimization via Partial Dependence Plots. CoRR abs/2111.04820 (2021) - [i36]Julia Moosbauer, Martin Binder, Lennart Schneider, Florian Pfisterer, Marc Becker, Michel Lang, Lars Kotthoff, Bernd Bischl:
Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers. CoRR abs/2111.14756 (2021) - 2020
- [j25]Andrea Bommert, Xudong Sun, Bernd Bischl, Jörg Rahnenführer, Michel Lang:
Benchmark for filter methods for feature selection in high-dimensional classification data. Comput. Stat. Data Anal. 143 (2020) - [c47]Martin Binder, Julia Moosbauer, Janek Thomas, Bernd Bischl:
Multi-objective hyperparameter tuning and feature selection using filter ensembles. GECCO 2020: 471-479 - [c46]Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl:
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models. xxAI@ICML 2020: 39-68 - [c45]Gunnar König, Christoph Molnar, Bernd Bischl, Moritz Grosse-Wentrup:
Relative Feature Importance. ICPR 2020: 9318-9325 - [c44]Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl:
A General Machine Learning Framework for Survival Analysis. ECML/PKDD (3) 2020: 158-173 - [c43]Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl:
Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges. PKDD/ECML Workshops 2020: 417-431 - [c42]Susanne Dandl, Christoph Molnar, Martin Binder, Bernd Bischl:
Multi-Objective Counterfactual Explanations. PPSN (1) 2020: 448-469 - [e1]Bernd Bischl, Oliver Guhr, Heidi Seibold, Peter Steinbach:
Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop, September 8+14, 2020, Virtual Conference. Proceedings of Machine Learning Research 141, PMLR 2020 [contents] - [i35]Susanne Dandl, Christoph Molnar, Martin Binder, Bernd Bischl:
Multi-Objective Counterfactual Explanations. CoRR abs/2004.11165 (2020) - [i34]Christoph Molnar, Gunnar König, Bernd Bischl, Giuseppe Casalicchio:
Model-agnostic Feature Importance and Effects with Dependent Features - A Conditional Subgroup Approach. CoRR abs/2006.04628 (2020) - [i33]Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl:
A General Machine Learning Framework for Survival Analysis. CoRR abs/2006.15442 (2020) - [i32]Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl:
Pitfalls to Avoid when Interpreting Machine Learning Models. CoRR abs/2007.04131 (2020) - [i31]Gunnar König, Christoph Molnar, Bernd Bischl, Moritz Grosse-Wentrup:
Relative Feature Importance. CoRR abs/2007.08283 (2020) - [i30]Raphael Sonabend, Franz J. Király, Andreas Bender, Bernd Bischl, Michel Lang:
mlr3proba: Machine Learning Survival Analysis in R. CoRR abs/2008.08080 (2020) - [i29]David Rügamer, Florian Pfisterer, Bernd Bischl:
Neural Mixture Distributional Regression. CoRR abs/2010.06889 (2020) - [i28]Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl:
Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges. CoRR abs/2010.09337 (2020) - [i27]Ashrya Agrawal, Florian Pfisterer, Bernd Bischl, Jiahao Chen, Srijan Sood, Sameena Shah, Francois Buet-Golfouse, Bilal A. Mateen, Sebastian J. Vollmer:
Debiasing classifiers: is reality at variance with expectation? CoRR abs/2011.02407 (2020) - [i26]Philipp Kopper, Sebastian Pölsterl, Christian Wachinger, Bernd Bischl, Andreas Bender, David Rügamer:
Semi-Structured Deep Piecewise Exponential Models. CoRR abs/2011.05824 (2020)
2010 – 2019
- 2019
- [j24]Matthias Schmid, Bernd Bischl, Hans A. Kestler:
Proceedings of Reisensburg 2016-2017. Comput. Stat. 34(3): 943-944 (2019) - [j23]Laura Beggel, Bernhard X. Kausler, Martin Schiegg, Michael Pfeiffer, Bernd Bischl:
Time series anomaly detection based on shapelet learning. Comput. Stat. 34(3): 945-976 (2019) - [j22]Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl:
OpenML: An R package to connect to the machine learning platform OpenML. Comput. Stat. 34(3): 977-991 (2019) - [j21]Philipp Probst, Anne-Laure Boulesteix, Bernd Bischl:
Tunability: Importance of Hyperparameters of Machine Learning Algorithms. J. Mach. Learn. Res. 20: 53:1-53:32 (2019) - [j20]Michel Lang, Martin Binder, Jakob Richter, Patrick Schratz, Florian Pfisterer, Stefan Coors, Quay Au, Giuseppe Casalicchio, Lars Kotthoff, Bernd Bischl:
mlr3: A modern object-oriented machine learning framework in R. J. Open Source Softw. 4(44): 1903 (2019) - [c41]Xudong Sun, Andrea Bommert, Florian Pfisterer, Jörg Rahnenführer, Michel Lang, Bernd Bischl:
High Dimensional Restrictive Federated Model Selection with Multi-objective Bayesian Optimization over Shifted Distributions. IntelliSys (1) 2019: 629-647