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, Springer Open.

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, Springer Open.

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.