Marco Loog

ORCID

Publication Activity (10 Years)

Years Active: 1999-2023
Publications (10 Years): 111

Top Venues

CoRR

ICPR

Pattern Recognit.

Pattern Recognit. Lett.

Utrecht University, Netherlands (PHD)

Education History

Utrecht University, Netherlands

PHD

Publications

Yuko Kato, David M. J. Tax, Marco Loog
A Review of Nonconformity Measures for Conformal Prediction in Regression. COPA (2023)
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)
Tom J. Viering, Marco Loog
The Shape of Learning Curves: A Review. IEEE Trans. Pattern Anal. Mach. Intell. 45 (6) (2023)
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)
Alexander Mey, Marco Loog
Improved Generalization in Semi-Supervised Learning: A Survey of Theoretical Results. IEEE Trans. Pattern Anal. Mach. Intell. 45 (4) (2023)
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)
Marco Loog, Jesse H. Krijthe, Manuele Bicego
Also for k-means: more data does not imply better performance. Mach. Learn. 112 (8) (2023)
Rolf A. N. Starre, Marco Loog, Frans A. Oliehoek
An Analysis of Abstracted Model-Based Reinforcement Learning. CoRR (2022)
Marco Loog, Tom J. Viering
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance. CoRR (2022)
Rolf A. N. Starre, Marco Loog, Frans A. Oliehoek
Model-Based Reinforcement Learning with State Abstraction: A Survey. BNAIC/BENELEARN (2022)
Chirag Raman, Hayley Hung, Marco Loog
Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecasts. CoRR (2022)
Yazhou Yang, Marco Loog
To Actively Initialize Active Learning. Pattern Recognit. 131 (2022)
Chirag Raman, Hayley Hung, Marco Loog
Social Processes: Self-supervised Meta-learning Over Conversational Groups for Forecasting Nonverbal Social Cues. ECCV Workshops (3) (2022)
Zhiyi Chen, Marco Loog, Jesse H. Krijthe
Explaining Two Strange Learning Curves. BNAIC/BENELEARN (2022)
Yuko Kato, David M. J. Tax, Marco Loog
A view on model misspecification in uncertainty quantification. CoRR (2022)
Yuko Kato, David M. J. Tax, Marco Loog
A View on Model Misspecification in Uncertainty Quantification. BNAIC/BENELEARN (2022)
Gijs van Tulder, Marco Loog
On the reusability of samples in active learning. CoRR (2022)
Ziqi Wang, Marco Loog
Enhancing Classifier Conservativeness and Robustness by Polynomiality. CoRR (2022)
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)
Ziqi Wang, Marco Loog
Enhancing Classifier Conservativeness and Robustness by Polynomiality. CVPR (2022)
Tom J. Viering, Marco Loog
The Shape of Learning Curves: a Review. CoRR (2021)
Wouter M. Kouw, Marco Loog
Robust domain-adaptive discriminant analysis. Pattern Recognit. Lett. 148 (2021)
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 (2021)
Wouter M. Kouw, Marco Loog
A Review of Domain Adaptation without Target Labels. IEEE Trans. Pattern Anal. Mach. Intell. 43 (3) (2021)
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)
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 (2021)
Marco Loog
Nearest Neighbor-based Importance Weighting. CoRR (2021)
Chirag Raman, Hayley Hung, Marco Loog
Social Processes: Self-Supervised Forecasting of Nonverbal Cues in Social Conversations. CoRR (2021)
Alexander Mey, Marco Loog
Consistency and Finite Sample Behavior of Binary Class Probability Estimation. AAAI (2021)
Alexander Mey, Tom Julian Viering, Marco Loog
A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization. IDA (2020)
Ziqi Wang, Marco Loog, Jan van Gemert
Respecting Domain Relations: Hypothesis Invariance for Domain Generalization. CoRR (2020)
Wouter M. Kouw, Marco Loog
Target Robust Discriminant Analysis. S+SSPR (2020)
Kasra Arnavaz, Aasa Feragen, Oswin Krause, Marco Loog
Bayesian Active Learning for Maximal Information Gain on Model Parameters. ICPR (2020)
Kanav Anand, Ziqi Wang, Marco Loog, Jan van Gemert
Black Magic in Deep Learning: How Human Skill Impacts Network Training. CoRR (2020)
Kanav Anand, Ziqi Wang, Marco Loog, Jan van Gemert
Black Magic in Deep Learning: How Human Skill Impacts Network Training. BMVC (2020)
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 (2020)
Julius von Kügelgen, Alexander Mey, Marco Loog, Bernhard Schölkopf
Semi-supervised learning, causality, and the conditional cluster assumption. UAI (2020)
Ziqi Wang, Marco Loog, Jan van Gemert
Respecting Domain Relations: Hypothesis Invariance for Domain Generalization. ICPR (2020)
Marco Loog, Tom J. Viering, Alexander Mey, Jesse H. Krijthe, David M. J. Tax
A Brief Prehistory of Double Descent. CoRR (2020)
Tom Julian Viering, Alexander Mey, Marco Loog
Making Learners (More) Monotone. IDA (2020)
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) (2019)
Lorenzo Bottarelli, Marco Loog
Gaussian process variance reduction by location selection. Pattern Recognit. Lett. 125 (2019)
Marco Loog, Tom J. Viering, Alexander Mey
Minimizers of the Empirical Risk and Risk Monotonicity. CoRR (2019)
Tom J. Viering, Alexander Mey, Marco Loog
Making Learners (More) Monotone. CoRR (2019)
Julius von Kügelgen, Alexander Mey, Marco Loog
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features. AISTATS (2019)
Tom J. Viering, Ziqi Wang, Marco Loog, Elmar Eisemann
How to Manipulate CNNs to Make Them Lie: the GradCAM Case. CoRR (2019)
Tom J. Viering, Alexander Mey, Marco Loog
Open Problem: Monotonicity of Learning. COLT (2019)
Wouter M. Kouw, Jesse H. Krijthe, Marco Loog
Robust Importance-Weighted Cross-Validation Under Sample Selection Bias. MLSP (2019)
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) (2019)
Wouter M. Kouw, Marco Loog, Lambertus W. Bartels, Adriënne M. Mendrik
Learning An Mr Acquisition-Invariant Representation Using Siamese Neural Networks. ISBI (2019)
Yazhou Yang, Marco Loog
Single shot active learning using pseudo annotators. Pattern Recognit. 89 (2019)
Marco Loog, Tom J. Viering, Alexander Mey
Minimizers of the Empirical Risk and Risk Monotonicity. NeurIPS (2019)
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 (2019)
Julius von Kügelgen, Marco Loog, Alexander Mey, Bernhard Schölkopf
Semi-Supervised Learning, Causality and the Conditional Cluster Assumption. CoRR (2019)
Alexander Mey, Marco Loog
Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results. CoRR (2019)
Alexander Mey, Tom J. Viering, Marco Loog
A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization. CoRR (2019)
Wouter M. Kouw, Marco Loog
A review of single-source unsupervised domain adaptation. CoRR (2019)
Alexander Mey, Marco Loog
Consistency and Finite Sample Behavior of Binary Class Probability Estimation. CoRR (2019)
Tom J. Viering, Jesse H. Krijthe, Marco Loog
Nuclear discrepancy for single-shot batch active learning. Mach. Learn. 108 (8-9) (2019)
Mina Sheikhalishahi, Majid Nateghizad, Fabio Martinelli, Zekeriya Erkin, Marco Loog
On the Statistical Detection of Adversarial Instances over Encrypted Data. STM (2019)
Wouter M. Kouw, Marco Loog
Effects of sampling skewness of the importance-weighted risk estimator on model selection. ICPR (2018)
Yazhou Yang, Marco Loog
Single Shot Active Learning using Pseudo Annotators. CoRR (2018)
Yazhou Yang, Marco Loog
A benchmark and comparison of active learning for logistic regression. Pattern Recognit. 83 (2018)
Wouter M. Kouw, Marco Loog, Wilbert Bartels, Adriënne M. Mendrik
Learning an MR acquisition-invariant representation using Siamese neural networks. CoRR (2018)
Yazhou Yang, Marco Loog
A variance maximization criterion for active learning. Pattern Recognit. 78 (2018)
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) (2018)
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. Medical Imaging: Image Processing (2018)
Wouter M. Kouw, Marco Loog
Effects of sampling skewness of the importance-weighted risk estimator on model selection. CoRR (2018)
Lex Razoux Schultz, Marco Loog, Peyman Mohajerin Esfahani
Distance Based Source Domain Selection for Sentiment Classification. CoRR (2018)
Julius von Kügelgen, Alexander Mey, Marco Loog
Semi-Generative Modelling: Domain Adaptation with Cause and Effect Features. CoRR (2018)
Wouter M. Kouw, Marco Loog
Target Contrastive Pessimistic Discriminant Analysis. CoRR (2018)
Lorenzo Bottarelli, Marco Loog
Gradient Descent for Gaussian Processes Variance Reduction. S+SSPR (2018)
Antonelli Mensi, Manuele Bicego, Pietro Lovato, Marco Loog, David M. J. Tax
Protein Remote Homology Detection Using Dissimilarity-Based Multiple Instance Learning. S+SSPR (2018)
Jesse H. Krijthe, Marco Loog
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning. NeurIPS (2018)
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 (2017)
Jesse H. Krijthe, Marco Loog
Robust semi-supervised least squares classification by implicit constraints. Pattern Recognit. 63 (2017)
Marco Loog, François Lauze
Scale-Regularized Filter Learning. CoRR (2017)
Marco Loog
Supervised Classification: Quite a Brief Overview. CoRR (2017)
Wouter M. Kouw, Marco Loog, Lambertus W. Bartels, Adriënne M. Mendrik
MR Acquisition-Invariant Representation Learning. CoRR (2017)
Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Marleen de Bruijne, Marco Loog
Label Stability in Multiple Instance Learning. CoRR (2017)
Wouter M. Kouw, Marco Loog
On reducing sampling variance in covariate shift using control variates. CoRR (2017)
Wouter M. Kouw, Marco Loog
Target contrastive pessimistic risk for robust domain adaptation. CoRR (2017)
Yazhou Yang, Marco Loog
Active Learning Using Uncertainty Information. CoRR (2017)
Yazhou Yang, Marco Loog
A Variance Maximization Criterion for Active Learning. CoRR (2017)
Marco Loog, Jesse H. Krijthe, Are Charles Jensen
On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL. CoRR (2017)
Amogh Gudi, Nicolai van Rosmalen, Marco Loog, Jan C. van Gemert
Object-Extent Pooling for Weakly Supervised Single-Shot Localization. CoRR (2017)
Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Jesper Holst Pedersen, Marco Loog, Marleen de Bruijne
Classification of COPD with Multiple Instance Learning. CoRR (2017)
Amogh Gudi, Nicolai van Rosmalen, Marco Loog, Jan C. van Gemert
Object-Extent Pooling for Weakly Supervised Single-Shot Localization. BMVC (2017)
Jesse H. Krijthe, Marco Loog
Projected estimators for robust semi-supervised classification. Mach. Learn. 106 (7) (2017)
Marco Loog, François Lauze
Supervised Scale-Regularized Linear Convolutionary Filters. BMVC (2017)
Tom J. Viering, Jesse H. Krijthe, Marco Loog
Nuclear Discrepancy for Active Learning. CoRR (2017)
Manuele Bicego, Marco Loog
Weighted K-Nearest Neighbor revisited. ICPR (2016)
Jesse H. Krijthe, Marco Loog
Optimistic semi-supervised least squares classification. ICPR (2016)
Jesse H. Krijthe, Marco Loog
The Pessimistic Limits of Margin-based Losses in Semi-supervised Learning. CoRR (2016)
Alexander Mey, Marco Loog
A soft-labeled self-training approach. ICPR (2016)
Marco Loog, Yazhou Yang
An empirical investigation into the inconsistency of sequential active learning. ICPR (2016)
Wouter M. Kouw, Laurens J. P. van der Maaten, Jesse H. Krijthe, Marco Loog
Feature-Level Domain Adaptation. J. Mach. Learn. Res. 17 (2016)
Yazhou Yang, Marco Loog
Active learning using uncertainty information. ICPR (2016)
Marco Loog
Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification. IEEE Trans. Pattern Anal. Mach. Intell. 38 (3) (2016)
Jesse H. Krijthe, Marco Loog
The Peaking Phenomenon in Semi-supervised Learning. CoRR (2016)

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Publication Activity (10 Years)

Top Topics

Top Venues

Marco Loog