13 citations · 50 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
Semi-Supervised Learning, Causality and the Conditional Cluster Assumption
Julius von Kügelgen, Alexander Mey, Marco Loog +1
While the success of semi-supervised learning (SSL) is still not fully understood, Schölkopf et al. (2012) have established a link to the principle of independent causal mechanisms…