Nuclear Discrepancy for Active Learning. - Tom J. Viering; Jesse H. Krijthe; Marco Loog (CoRR, 2017)

Open Problem: Monotonicity of Learning. - Tom J. Viering; Alexander Mey; Marco Loog (COLT, 2019)

Minimizers of the Empirical Risk and Risk Monotonicity. - Marco Loog; Tom J. Viering; Alexander Mey (NeurIPS, 2019)

How to Manipulate CNNs to Make Them Lie: the GradCAM Case. - Tom J. Viering; Ziqi Wang 004; Marco Loog; Elmar Eisemann (CoRR, 2019)

Nuclear discrepancy for single-shot batch active learning. - Tom J. Viering; Jesse H. Krijthe; Marco Loog (Mach. Learn., 2019)

A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization. - Alexander Mey; Tom Julian Viering; Marco Loog (IDA, 2020)

Making Learners (More) Monotone. - Tom Julian Viering; Alexander Mey; Marco Loog (IDA, 2020)

A Brief Prehistory of Double Descent. - Marco Loog; Tom J. Viering; Alexander Mey; Jesse H. Krijthe; David M. J. Tax (CoRR, 2020)

LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks. - Felix Mohr; Tom J. Viering; Marco Loog; Jan N. van Rijn (ECML/PKDD (5), 2022)

A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance. - Marco Loog; Tom J. Viering (CoRR, 2022)

On Safety in Machine Learning. - Tom Julian Viering (2023)

The Shape of Learning Curves: A Review. - Tom J. Viering; Marco Loog (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

The unreasonable effectiveness of early discarding after one epoch in neural network hyperparameter optimization. - Romain Egele; Felix Mohr; Tom J. Viering; Prasanna Balaprakash (Neurocomputing, 2024)

Learning Learning Curves. - O. Taylan Turan; David M. J. Tax; Tom J. Viering; Marco Loog (Pattern Anal. Appl., 2025)

LCDB 1.1: A Database Illustrating Learning Curves Are More Ill-Behaved Than Previously Thought. - Cheng Yan; Felix Mohr; Tom J. Viering (CoRR, 2025)