30 citations · 106 across the 11 of their papers we have counts for
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cs.LG2019
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…
cs.LG2019
Consistency and Finite Sample Behavior of Binary Class Probability Estimation
Alexander Mey, Marco Loog
In this work we investigate to which extent one can recover class probabilities within the empirical risk minimization (ERM) paradigm. The main aim of our paper is to extend existi…
cs.LG2019★ 9 cited
Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results
Alexander Mey, Marco Loog
Semi-supervised learning is a setting in which one has labeled and unlabeled data available. In this survey we explore different types of theoretical results when one uses unlabele…