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.

T.J. Viering (2023), On Safety in Machine Learning, PhD Thesis Delft University of Technology.

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

Alexander Mey, Tom Julian Viering, Marco Loog (2020), A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization, Michael R. Berthold, Ad Feelders, Georg Krempl (Eds.), In Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Proceedings Volume 12080 p.326-338, SpringerOpen.

Marco Loog, Tom Viering, Alexander Mey, Jesse H. Krijthe, David M. J. Tax (2020), A brief prehistory of double descent, In Proceedings of the National Academy of Sciences of the United States of America p.10625-10626.

Katja Geertruida Schmahl, Tom Julian Viering, Stavros Makrodimitris, Arman Naseri Jahfari, David Tax, Marco Loog (2020), Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings, In Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science p.94-103, Association for Computational Linguistics.

Tom Julian Viering, Alexander Mey, Marco Loog (2020), Making Learners (More) Monotone, Michael R. Berthold, Ad Feelders, Georg Krempl (Eds.), In Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Proceedings Volume 12080 p.535-547, SpringerOpen.

M. Loog, T.J. Viering, A. Mey (2019), Minimizers of the empirical risk and risk monotonicity, In Neural Information Processing Systems.

Tom J. Viering, Jesse H. Krijthe, Marco Loog (2019), Nuclear discrepancy for single-shot batch active learning, In Machine Learning Volume 108 p.1561-1599.

Tom Viering, Jesse Krijthe, Marco Loog (2017), Generalization Bound Minimization for Active Learning, Benelearn 2017 p.108-109.