3 citations · 3 across the 1 of their papers we have counts for
8 papers
Loss Bounds for Approximate Influence-Based Abstraction
Elena Congeduti, Alexander Mey, Frans A. Oliehoek
Sequential decision making techniques hold great promise to improve the performance of many real-world systems, but computational complexity hampers their principled application. I…
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…