17 citations · 45 across the 7 of their papers we have counts for
8 papers
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance
Marco Loog, Tom Viering
Plotting a learner's generalization performance against the training set size results in a so-called learning curve. This tool, providing insight in the behavior of the learner, is…
The Shape of Learning Curves: a Review
Tom Viering, Marco Loog
Learning curves provide insight into the dependence of a learner's generalization performance on the training set size. This important tool can be used for model selection, to pred…
A Brief Prehistory of Double Descent
Marco Loog, Tom Viering, Alexander Mey +2
In their thought-provoking paper [1], Belkin et al. illustrate and discuss the shape of risk curves in the context of modern high-complexity learners. Given a fixed training sample…
Making Learners (More) Monotone
Tom J. Viering, Alexander Mey, Marco Loog
Learning performance can show non-monotonic behavior. That is, more data does not necessarily lead to better models, even on average. We propose three algorithms that take a superv…
How to Manipulate CNNs to Make Them Lie: the GradCAM Case
Tom Viering, Ziqi Wang, Marco Loog +1
Recently many methods have been introduced to explain CNN decisions. However, it has been shown that some methods can be sensitive to manipulation of the input. We continue this li…
Minimizers of the Empirical Risk and Risk Monotonicity
Marco Loog, Tom Viering, Alexander Mey
Plotting a learner's average performance against the number of training samples results in a learning curve. Studying such curves on one or more data sets is a way to get to a bett…
A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization
Alexander Mey, Tom Viering, Marco Loog
Manifold regularization is a commonly used technique in semi-supervised learning. It enforces the classification rule to be smooth with respect to the data-manifold. Here, we deriv…
Nuclear Discrepancy for Active Learning
Tom J. Viering, Jesse H. Krijthe, Marco Loog
Active learning algorithms propose which unlabeled objects should be queried for their labels to improve a predictive model the most. We study active learners that minimize general…