3 citations · 4 across the 2 of their papers we have counts for
8 papers · 1 filter
Invariant Causal Prediction with Local Models
Alexander Mey, Rui Manuel Castro
We consider the task of identifying the causal parents of a target variable among a set of candidates from observational data. Our main assumption is that the candidate variables a…
A Note on High-Probability versus In-Expectation Guarantees of Generalization Bounds in Machine Learning
Alexander Mey
Statistical machine learning theory often tries to give generalization guarantees of machine learning models. Those models naturally underlie some fluctuation, as they are based on…
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…
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…