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Marco Loog
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- affiliation: Delft University of Technology, Netherlands
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2020 – today
- 2025
- [j61]O. Taylan Turan, David M. J. Tax, Tom J. Viering, Marco Loog:
Learning Learning Curves. Pattern Anal. Appl. 28(1): 15 (2025) - 2024
- [i57]Yuko Kato, David M. J. Tax, Marco Loog:
Inductive Conformal Prediction under Data Scarcity: Exploring the Impacts of Nonconformity Measures. CoRR abs/2410.09894 (2024) - 2023
- [j60]Marco Loog
, Jesse H. Krijthe, Manuele Bicego:
Also for k-means: more data does not imply better performance. Mach. Learn. 112(8): 3033-3050 (2023) - [j59]Alexander Mey
, Marco Loog
:
Improved Generalization in Semi-Supervised Learning: A Survey of Theoretical Results. IEEE Trans. Pattern Anal. Mach. Intell. 45(4): 4747-4767 (2023) - [j58]Tom J. Viering
, Marco Loog
:
The Shape of Learning Curves: A Review. IEEE Trans. Pattern Anal. Mach. Intell. 45(6): 7799-7819 (2023) - [j57]Rolf A. N. Starre, Marco Loog, Elena Congeduti, Frans A. Oliehoek:
An Analysis of Model-Based Reinforcement Learning From Abstracted Observations. Trans. Mach. Learn. Res. 2023 (2023) - [c129]Yuko Kato, David M. J. Tax, Marco Loog:
A Review of Nonconformity Measures for Conformal Prediction in Regression. COPA 2023: 369-383 - [c128]Chirag Raman, Alec Nonnemaker, Amelia Villegas-Morcillo, Hayley Hung, Marco Loog:
Why Did This Model Forecast This Future? Information-Theoretic Saliency for Counterfactual Explanations of Probabilistic Regression Models. NeurIPS 2023 - [c127]Soufiane Mourragui, Marco Loog, Mirrelijn M. van Nee, Mark A. van de Wiel, Marcel J. T. Reinders, Lodewyk F. A. Wessels:
Percolate: An Exponential Family JIVE Model to Design DNA-Based Predictors of Drug Response. RECOMB 2023: 120-138 - 2022
- [j56]Yazhou Yang
, Marco Loog
:
To Actively Initialize Active Learning. Pattern Recognit. 131: 108836 (2022) - [c126]Zhiyi Chen, Marco Loog, Jesse H. Krijthe:
Explaining Two Strange Learning Curves. BNAIC/BENELEARN 2022: 16-30 - [c125]Yuko Kato, David M. J. Tax, Marco Loog:
A View on Model Misspecification in Uncertainty Quantification. BNAIC/BENELEARN 2022: 65-77 - [c124]Rolf A. N. Starre, Marco Loog, Frans A. Oliehoek
:
Model-Based Reinforcement Learning with State Abstraction: A Survey. BNAIC/BENELEARN 2022: 133-148 - [c123]Ziqi Wang, Marco Loog:
Enhancing Classifier Conservativeness and Robustness by Polynomiality. CVPR 2022: 13317-13326 - [c122]Chirag Raman
, Hayley Hung
, Marco Loog
:
Social Processes: Self-supervised Meta-learning Over Conversational Groups for Forecasting Nonverbal Social Cues. ECCV Workshops (3) 2022: 639-659 - [c121]Felix Mohr, Tom J. Viering, Marco Loog, Jan N. van Rijn:
LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks. ECML/PKDD (5) 2022: 3-19 - [i56]Ziqi Wang, Marco Loog:
Enhancing Classifier Conservativeness and Robustness by Polynomiality. CoRR abs/2203.12693 (2022) - [i55]Chirag Raman, Hayley Hung, Marco Loog:
Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecasts. CoRR abs/2206.00679 (2022) - [i54]Gijs van Tulder, Marco Loog:
On the reusability of samples in active learning. CoRR abs/2206.06276 (2022) - [i53]Rolf A. N. Starre, Marco Loog, Frans A. Oliehoek:
An Analysis of Abstracted Model-Based Reinforcement Learning. CoRR abs/2208.14407 (2022) - [i52]Yuko Kato, David M. J. Tax, Marco Loog:
A view on model misspecification in uncertainty quantification. CoRR abs/2210.16938 (2022) - [i51]Marco Loog, Tom J. Viering:
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance. CoRR abs/2211.14061 (2022) - 2021
- [j55]Wouter M. Kouw
, Marco Loog
:
A Review of Domain Adaptation without Target Labels. IEEE Trans. Pattern Anal. Mach. Intell. 43(3): 766-785 (2021) - [j54]Wouter M. Kouw
, Marco Loog:
Robust domain-adaptive discriminant analysis. Pattern Recognit. Lett. 148: 107-113 (2021) - [j53]Silvia L. Pintea
, Nergis Tomen, Stanley F. Goes, Marco Loog, Jan C. van Gemert:
Resolution Learning in Deep Convolutional Networks Using Scale-Space Theory. IEEE Trans. Image Process. 30: 8342-8353 (2021) - [c120]Alexander Mey, Marco Loog:
Consistency and Finite Sample Behavior of Binary Class Probability Estimation. AAAI 2021: 8967-8974 - [c119]Burak Yildiz
, Hayley Hung, Jesse H. Krijthe
, Cynthia C. S. Liem
, Marco Loog
, Gosia Migut, Frans A. Oliehoek
, Annibale Panichella
, Przemyslaw Pawelczak
, Stjepan Picek
, Mathijs de Weerdt
, Jan van Gemert
:
ReproducedPapers.org: Openly Teaching and Structuring Machine Learning Reproducibility. RRPR 2021: 3-11 - [i50]Marco Loog:
Nearest Neighbor-based Importance Weighting. CoRR abs/2102.02291 (2021) - [i49]Tom J. Viering, Marco Loog:
The Shape of Learning Curves: a Review. CoRR abs/2103.10948 (2021) - [i48]Silvia L. Pintea, Nergis Tomen, Stanley F. Goes, Marco Loog, Jan C. van Gemert:
Resolution learning in deep convolutional networks using scale-space theory. CoRR abs/2106.03412 (2021) - [i47]Chirag Raman, Hayley Hung, Marco Loog:
Social Processes: Self-Supervised Forecasting of Nonverbal Cues in Social Conversations. CoRR abs/2107.13576 (2021) - 2020
- [c118]Kanav Anand, Ziqi Wang, Marco Loog, Jan van Gemert:
Black Magic in Deep Learning: How Human Skill Impacts Network Training. BMVC 2020 - [c117]Ziqi Wang, Marco Loog, Jan van Gemert:
Respecting Domain Relations: Hypothesis Invariance for Domain Generalization. ICPR 2020: 9756-9763 - [c116]Kasra Arnavaz
, Aasa Feragen
, Oswin Krause
, Marco Loog:
Bayesian Active Learning for Maximal Information Gain on Model Parameters. ICPR 2020: 10524-10531 - [c115]Alexander Mey
, Tom Julian Viering
, Marco Loog
:
A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization. IDA 2020: 326-338 - [c114]Tom Julian Viering
, Alexander Mey
, Marco Loog
:
Making Learners (More) Monotone. IDA 2020: 535-547 - [c113]Wouter M. Kouw
, Marco Loog:
Target Robust Discriminant Analysis. S+SSPR 2020: 3-13 - [c112]Julius von Kügelgen, Alexander Mey
, Marco Loog, Bernhard Schölkopf:
Semi-supervised learning, causality, and the conditional cluster assumption. UAI 2020: 1-10 - [i46]Marco Loog, Tom J. Viering, Alexander Mey, Jesse H. Krijthe, David M. J. Tax:
A Brief Prehistory of Double Descent. CoRR abs/2004.04328 (2020) - [i45]Kanav Anand, Ziqi Wang, Marco Loog, Jan van Gemert:
Black Magic in Deep Learning: How Human Skill Impacts Network Training. CoRR abs/2008.05981 (2020) - [i44]Ziqi Wang, Marco Loog, Jan van Gemert:
Respecting Domain Relations: Hypothesis Invariance for Domain Generalization. CoRR abs/2010.07591 (2020) - [i43]Burak Yildiz, Hayley Hung, Jesse H. Krijthe, Cynthia C. S. Liem, Marco Loog, Gosia Migut, Frans A. Oliehoek, Annibale Panichella, Przemyslaw Pawelczak, Stjepan Picek, Mathijs de Weerdt, Jan van Gemert:
ReproducedPapers.org: Openly teaching and structuring machine learning reproducibility. CoRR abs/2012.01172 (2020)
2010 – 2019
- 2019
- [j52]Soufiane Mourragui, Marco Loog, Mark A. van de Wiel
, Marcel J. T. Reinders, Lodewyk F. A. Wessels:
PRECISE: a domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors. Bioinform. 35(14): i510-i519 (2019) - [j51]Tom J. Viering
, Jesse H. Krijthe
, Marco Loog
:
Nuclear discrepancy for single-shot batch active learning. Mach. Learn. 108(8-9): 1561-1599 (2019) - [j50]Yuan Zeng
, Jan C. A. van der Lubbe, Marco Loog:
Multi-scale convolutional neural network for pixel-wise reconstruction of Van Gogh's drawings. Mach. Vis. Appl. 30(7-8): 1229-1241 (2019) - [j49]Yazhou Yang
, Marco Loog
:
Single shot active learning using pseudo annotators. Pattern Recognit. 89: 22-31 (2019) - [j48]Lorenzo Bottarelli
, Marco Loog
:
Gaussian process variance reduction by location selection. Pattern Recognit. Lett. 125: 727-734 (2019) - [j47]Antonella Mensi
, Manuele Bicego, Pietro Lovato, Marco Loog
, David M. J. Tax:
A dissimilarity-based multiple instance learning approach for protein remote homology detection. Pattern Recognit. Lett. 128: 231-236 (2019) - [c111]Julius von Kügelgen, Alexander Mey
, Marco Loog:
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features. AISTATS 2019: 1361-1369 - [c110]Tom J. Viering, Alexander Mey
, Marco Loog:
Open Problem: Monotonicity of Learning. COLT 2019: 3198-3201 - [c109]Wouter M. Kouw
, Marco Loog, Lambertus W. Bartels, Adriënne M. Mendrik:
Learning An Mr Acquisition-Invariant Representation Using Siamese Neural Networks. ISBI 2019: 364-367 - [c108]Wouter M. Kouw
, Jesse H. Krijthe, Marco Loog:
Robust Importance-Weighted Cross-Validation Under Sample Selection Bias. MLSP 2019: 1-6 - [c107]Marco Loog, Tom J. Viering, Alexander Mey
:
Minimizers of the Empirical Risk and Risk Monotonicity. NeurIPS 2019: 7476-7485 - [c106]Mina Sheikhalishahi, Majid Nateghizad, Fabio Martinelli, Zekeriya Erkin
, Marco Loog:
On the Statistical Detection of Adversarial Instances over Encrypted Data. STM 2019: 71-88 - [i42]Wouter M. Kouw, Marco Loog:
A review of single-source unsupervised domain adaptation. CoRR abs/1901.05335 (2019) - [i41]Julius von Kügelgen, Marco Loog, Alexander Mey, Bernhard Schölkopf:
Semi-Supervised Learning, Causality and the Conditional Cluster Assumption. CoRR abs/1905.12081 (2019) - [i40]Alexander Mey, Tom J. Viering, Marco Loog:
A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization. CoRR abs/1906.06100 (2019) - [i39]Marco Loog, Tom J. Viering, Alexander Mey:
Minimizers of the Empirical Risk and Risk Monotonicity. CoRR abs/1907.05476 (2019) - [i38]Tom J. Viering
, Ziqi Wang, Marco Loog, Elmar Eisemann:
How to Manipulate CNNs to Make Them Lie: the GradCAM Case. CoRR abs/1907.10901 (2019) - [i37]Alexander Mey
, Marco Loog:
Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results. CoRR abs/1908.09574 (2019) - [i36]Alexander Mey, Marco Loog:
Consistency and Finite Sample Behavior of Binary Class Probability Estimation. CoRR abs/1908.11823 (2019) - [i35]Tom J. Viering, Alexander Mey, Marco Loog:
Making Learners (More) Monotone. CoRR abs/1911.11030 (2019) - 2018
- [j46]Erik J. Bekkers
, Marco Loog, Bart M. ter Haar Romeny
, Remco Duits:
Template Matching via Densities on the Roto-Translation Group. IEEE Trans. Pattern Anal. Mach. Intell. 40(2): 452-466 (2018) - [j45]Yazhou Yang
, Marco Loog:
A variance maximization criterion for active learning. Pattern Recognit. 78: 358-370 (2018) - [j44]Yazhou Yang
, Marco Loog
:
A benchmark and comparison of active learning for logistic regression. Pattern Recognit. 83: 401-415 (2018) - [c105]Wouter M. Kouw
, Marco Loog:
Effects of sampling skewness of the importance-weighted risk estimator on model selection. ICPR 2018: 1468-1473 - [c104]Marijn van Stralen, Y. Zhou, P. J. Wozny, Peter R. Seevinck, Marco Loog:
Contextual loss functions for optimization of convolutional neural networks generating pseudo CTs from MRI. Image Processing 2018: 105741N - [c103]Jesse H. Krijthe, Marco Loog:
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning. NeurIPS 2018: 1795-1804 - [c102]Antonella Mensi
, Manuele Bicego, Pietro Lovato, Marco Loog, David M. J. Tax:
Protein Remote Homology Detection Using Dissimilarity-Based Multiple Instance Learning. S+SSPR 2018: 119-129 - [c101]Lorenzo Bottarelli
, Marco Loog:
Gradient Descent for Gaussian Processes Variance Reduction. S+SSPR 2018: 160-169 - [i34]Wouter M. Kouw, Marco Loog:
Effects of sampling skewness of the importance-weighted risk estimator on model selection. CoRR abs/1804.07344 (2018) - [i33]Yazhou Yang, Marco Loog:
Single Shot Active Learning using Pseudo Annotators. CoRR abs/1805.06660 (2018) - [i32]Wouter M. Kouw, Marco Loog:
Target Contrastive Pessimistic Discriminant Analysis. CoRR abs/1806.09463 (2018) - [i31]Julius von Kügelgen, Alexander Mey, Marco Loog:
Semi-Generative Modelling: Domain Adaptation with Cause and Effect Features. CoRR abs/1807.07879 (2018) - [i30]Lex Razoux Schultz, Marco Loog, Peyman Mohajerin Esfahani:
Distance Based Source Domain Selection for Sentiment Classification. CoRR abs/1808.09271 (2018) - [i29]Wouter M. Kouw, Marco Loog, Wilbert Bartels, Adriënne M. Mendrik:
Learning an MR acquisition-invariant representation using Siamese neural networks. CoRR abs/1810.07430 (2018) - 2017
- [j43]Jesse H. Krijthe
, Marco Loog:
Projected estimators for robust semi-supervised classification. Mach. Learn. 106(7): 993-1008 (2017) - [j42]Jesse H. Krijthe, Marco Loog:
Robust semi-supervised least squares classification by implicit constraints. Pattern Recognit. 63: 115-126 (2017) - [j41]Jianxin Wu, Xiang Bai, Marco Loog, Fabio Roli
, Zhi-Hua Zhou:
Editorial of the Special Issue on Multi-instance Learning in Pattern Recognition and Vision. Pattern Recognit. 71: 444-445 (2017) - [c100]Amogh Gudi, Nicolai van Rosmalen, Marco Loog, Jan C. van Gemert:
Object-Extent Pooling for Weakly Supervised Single-Shot Localization. BMVC 2017 - [c99]Marco Loog, François Lauze:
Supervised Scale-Regularized Linear Convolutionary Filters. BMVC 2017 - [i28]Yazhou Yang, Marco Loog:
Active Learning Using Uncertainty Information. CoRR abs/1702.08540 (2017) - [i27]Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Jesper Holst Pedersen, Marco Loog, Marleen de Bruijne:
Classification of COPD with Multiple Instance Learning. CoRR abs/1703.04980 (2017) - [i26]Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Marleen de Bruijne, Marco Loog:
Label Stability in Multiple Instance Learning. CoRR abs/1703.04986 (2017) - [i25]Tom J. Viering, Jesse H. Krijthe, Marco Loog:
Nuclear Discrepancy for Active Learning. CoRR abs/1706.02645 (2017) - [i24]Yazhou Yang, Marco Loog:
A Variance Maximization Criterion for Active Learning. CoRR abs/1706.07642 (2017) - [i23]Wouter M. Kouw, Marco Loog:
Target contrastive pessimistic risk for robust domain adaptation. CoRR abs/1706.08082 (2017) - [i22]Marco Loog, François Lauze:
Scale-Regularized Filter Learning. CoRR abs/1707.02813 (2017) - [i21]Marco Loog, Jesse H. Krijthe, Are Charles Jensen:
On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL. CoRR abs/1707.04025 (2017) - [i20]Amogh Gudi, Nicolai van Rosmalen, Marco Loog, Jan C. van Gemert:
Object-Extent Pooling for Weakly Supervised Single-Shot Localization. CoRR abs/1707.06180 (2017) - [i19]Wouter M. Kouw, Marco Loog, Lambertus W. Bartels, Adriënne M. Mendrik:
MR Acquisition-Invariant Representation Learning. CoRR abs/1709.07944 (2017) - [i18]Wouter M. Kouw, Marco Loog:
On reducing sampling variance in covariate shift using control variates. CoRR abs/1710.06514 (2017) - [i17]Marco Loog:
Supervised Classification: Quite a Brief Overview. CoRR abs/1710.09230 (2017) - 2016
- [j40]Wouter M. Kouw, Laurens J. P. van der Maaten, Jesse H. Krijthe, Marco Loog:
Feature-Level Domain Adaptation. J. Mach. Learn. Res. 17: 171:1-171:32 (2016) - [j39]Marco Loog
:
Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification. IEEE Trans. Pattern Anal. Mach. Intell. 38(3): 462-475 (2016) - [j38]Veronika Cheplygina
, David M. J. Tax, Marco Loog:
Dissimilarity-Based Ensembles for Multiple Instance Learning. IEEE Trans. Neural Networks Learn. Syst. 27(6): 1379-1391 (2016) - [c98]Yenisel Plasencia Calana
, Yan Li, Robert P. W. Duin, Mauricio Orozco-Alzate
, Marco Loog, Edel B. García Reyes
:
A Compact Representation of Multiscale Dissimilarity Data by Prototype Selection. CIARP 2016: 150-157 - [c97]Yuan Zeng, Jiexiong Tang, Jan C. A. van der Lubbe, Marco Loog:
Learning Algorithms for Digital Reconstruction of Van Gogh's Drawings. EuroMed (1) 2016: 322-333 - [c96]Jesse H. Krijthe, Marco Loog:
Reproducible Pattern Recognition Research: The Case of Optimistic SSL. RRPR@ICPR 2016: 48-59 - [c95]Marco Loog, Yazhou Yang:
An empirical investigation into the inconsistency of sequential active learning. ICPR 2016: 210-215 - [c94]Wouter M. Kouw
, Marco Loog:
On regularization parameter estimation under covariate shift. ICPR 2016: 426-431 - [c93]Manuele Bicego, Marco Loog:
Weighted K-Nearest Neighbor revisited. ICPR 2016: 1642-1647 - [c92]Jesse H. Krijthe, Marco Loog:
Optimistic semi-supervised least squares classification. ICPR 2016: 1677-1682 - [c91]Alexander Mey
, Marco Loog:
A soft-labeled self-training approach. ICPR 2016: 2604-2609 - [c90]Yazhou Yang, Marco Loog:
Active learning using uncertainty information. ICPR 2016: 2646-2651 - [c89]Jesse H. Krijthe, Marco Loog:
The Peaking Phenomenon in Semi-supervised Learning. S+SSPR 2016: 299-309 - [p2]Marco Loog, Jesse H. Krijthe, Are Charles Jensen:
On Measuring and Quantifying Performance: error rates, surrogate Loss, and an Example in Semi-Supervised Learning. Handbook of Pattern Recognition and Computer Vision 2016: 53-68 - [e9]Gustavo Carneiro
, Diana Mateus, Loïc Peter, Andrew P. Bradley, João Manuel R. S. Tavares, Vasileios Belagiannis, João Paulo Papa, Jacinto C. Nascimento, Marco Loog, Zhi Lu, Jaime S. Cardoso, Julien Cornebise:
Deep Learning and Data Labeling for Medical Applications - First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings. Lecture Notes in Computer Science 10008, 2016, ISBN 978-3-319-46975-1 [contents] - [e8]Antonio Robles-Kelly, Marco Loog, Battista Biggio, Francisco Escolano, Richard C. Wilson:
Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2016, Mérida, Mexico, November 29 - December 2, 2016, Proceedings. Lecture Notes in Computer Science 10029, 2016, ISBN 978-3-319-49054-0 [contents] - [i16]Jesse H. Krijthe, Marco Loog:
Projected Estimators for Robust Semi-supervised Classification. CoRR abs/1602.07865 (2016) - [i15]Erik J. Bekkers, Marco Loog, Bart M. ter Haar Romeny, Remco Duits:
Template Matching on the Roto-Translation Group. CoRR abs/1603.03304 (2016) - [i14]Wouter M. Kouw, Marco Loog:
On Regularization Parameter Estimation under Covariate Shift. CoRR abs/1608.00250 (2016) - [i13]Jesse H. Krijthe, Marco Loog:
Optimistic Semi-supervised Least Squares Classification. CoRR abs/1610.03713 (2016) - [i12]Jesse H. Krijthe, Marco Loog:
The Peaking Phenomenon in Semi-supervised Learning. CoRR abs/1610.05160 (2016) - [i11]Jesse H. Krijthe, Marco Loog:
Reproducible Pattern Recognition Research: The Case of Optimistic SSL. CoRR abs/1612.08650 (2016) - [i10]Jesse H. Krijthe, Marco Loog:
The Pessimistic Limits of Margin-based Losses in Semi-supervised Learning. CoRR abs/1612.08875 (2016) - 2015
- [j37]Veronika Cheplygina
, David M. J. Tax, Marco Loog:
Multiple instance learning with bag dissimilarities. Pattern Recognit. 48(1): 264-275 (2015) - [j36]Ethem Alpaydin
, Veronika Cheplygina
, Marco Loog, David M. J. Tax:
Single- vs. multiple-instance classification. Pattern Recognit. 48(9): 2831-2838 (2015) - [j35]Veronika Cheplygina
, David M. J. Tax, Marco Loog:
On classification with bags, groups and sets. Pattern Recognit. Lett. 59: 11-17 (2015) - [j34]