Yuko Kato, David M.J. Tax, Marco Loog (2023), A View on Model Misspecification in Uncertainty Quantification, Toon Calders, Bart Goethals, Celine Vens, Jefrey Lijffijt (Eds.), In Artificial Intelligence and Machine Learning - 34th Joint Benelux Conference, BNAIC/Benelearn 2022, Revised Selected Papers p.65-77, Springer.

Marco Loog, Jesse H. Krijthe, Manuele Bicego (2023), Also for k-means: more data does not imply better performance, In Machine Learning Volume 112 p.3033-3050.

R.A.N. Starre, M. Loog, E. Congeduti, F.A. Oliehoek (2023), An Analysis of Model-Based Reinforcement Learning From Abstracted Observations, In Transactions on Machine Learning Research.

Felix Mohr, Tom J. Viering, Marco Loog, Jan N. van Rijn (2023), LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks, Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas (Eds.), In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Proceedings p.3-19, Springer.

Soufiane M.C. Mourragui, Marco Loog, Mirrelijn van Nee, Mark A.van de Wiel, Marcel J.T. Reinders, Lodewyk F.A. Wessels (2023), Percolate: An Exponential Family JIVE Model to Design DNA-Based Predictors of Drug Response, Haixu Tang (Eds.), In Research in Computational Molecular Biology - 27th Annual International Conference, RECOMB 2023, Proceedings p.120-138, Springer.

Chirag Raman, Hayley Hung, Marco Loog (2023), Social Processes: Self-supervised Meta-learning Over Conversational Groups for Forecasting Nonverbal Social Cues, Leonid Karlinsky, Tomer Michaeli, Ko Nishino (Eds.), In Computer Vision – ECCV 2022 Workshops, Proceedings p.639-659, Springer.

Tom Viering, Marco Loog (2023), The Shape of Learning Curves: A Review, In IEEE Transactions on Pattern Analysis and Machine Intelligence p.7799-7819.

Ziqi Wang, Marco Loog (2022), Enhancing Classifier Conservativeness and Robustness by Polynomiality, In Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 p.13317-13326, IEEE.

Alexander Mey, Marco Loog (2022), Improved Generalization in Semi-Supervised Learning: A Survey of Theoretical Results, In IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 45 p.4747-4767.

R.A.N. Starre, M. Loog, F.A. Oliehoek (2022), Model-Based Reinforcement Learning with State Abstraction: A Survey, Toon Calders, Bart Goethals, Celine Vens, Jefrey Lijffijt (Eds.), In 34th Benelux Conference on Artificial Intelligence (BNAIC) and the 30th Belgian Dutch Conference on Machine Learning (Benelearn) p.133–148.