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.

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.

Yazhou Yang, Marco Loog (2022), To Actively Initialize Active Learning, In Pattern Recognition Volume 131.