4 citations · 9 across the 11 of their papers we have counts for
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Inductive Conformal Prediction under Data Scarcity: Exploring the Impacts of Nonconformity Measures
Yuko Kato, David M. J. Tax, Marco Loog
Conformal prediction, which makes no distributional assumptions about the data, has emerged as a powerful and reliable approach to uncertainty quantification in practical applicati…
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…
A view on model misspecification in uncertainty quantification
Yuko Kato, David M. J. Tax, Marco Loog
Estimating uncertainty of machine learning models is essential to assess the quality of the predictions that these models provide. However, there are several factors that influence…
Enhancing Classifier Conservativeness and Robustness by Polynomiality
Ziqi Wang, Marco Loog
We illustrate the detrimental effect, such as overconfident decisions, that exponential behavior can have in methods like classical LDA and logistic regression. We then show how po…
Nearest Neighbor-based Importance Weighting
Marco Loog
Importance weighting is widely applicable in machine learning in general and in techniques dealing with data covariate shift problems in particular. A novel, direct approach to det…
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…