Wouter M.R. Kant, Wieske K. de Swart, Jim M. Smit, Marco Loog, Jesse H. Krijthe (2026), Applications and implicit assumptions in dementia risk scores: A scoping review of the LIBRA score, In Journal of Alzheimer's Disease Reports.

O. Taylan Turan, Marco Loog, David M.J. Tax (2026), Generalization performance distributions along learning curves, In Pattern Recognition Letters Volume 201 p.29-36.

O. Taylan Turan, Marco Loog, David M.J. Tax (2026), On Sample-Wise Strict Monotonicity with a Gradient Update, Mitra Baratchi, Jan N. van Rijn, Siegfried Nijssen (Eds.), In Advances in Intelligent Data Analysis XXIV - 24th International Symposium on Intelligent Data Analysis, IDA 2026, Leiden, Proceedings p.72-83, Springer.

Michał Grzejdziak-Zdziarski, David M.J. Tax, Marco Loog (2026), The Vanishing Empirical Variance in Randomly Initialized Deep ReLU Networks, Rita P. Ribeiro, Bernhard Pfahringer, Nathalie Japkowicz, Pedro Larrañaga, Alípio M. Jorge, Carlos Soares, Pedro H. Abreu, João Gama (Eds.), In Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2025, Proceedings p.362-379, Springer.

Wieske K. de Swart, Marco Loog, Jesse H. Krijthe (2025), A comparative study of methods for dynamic survival analysis, In Frontiers in Neurology Volume 16.

Marco Loog, Jesse H. Krijthe, Manuele Bicego (2025), Counterintuitive Behavior of Clustering Quality: Findings for K-Means on Synthetic and Real Data, Georg Krempl, Kai Puolamäki, Ioanna Miliou (Eds.), In Advances in Intelligent Data Analysis XXIII - 23rd International Symposium on Intelligent Data Analysis, IDA 2025, Proceedings p.154-166, Springer.

O. Taylan Turan, David M.J. Tax, Tom J. Viering, Marco Loog (2025), Learning Learning Curves, In Pattern Analysis and Applications Volume 28.

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.

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.