254 citations · 494 across the 16 of their papers we have counts for
17 papers
The no-free-lunch theorems of supervised learning
Tom F. Sterkenburg, Peter D. Grünwald
The no-free-lunch theorems promote a skeptical conclusion that all possible machine learning algorithms equally lack justification. But how could this leave room for a learning the…
PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast rate bounds that handle general VC classes
Peter Grünwald, Thomas Steinke, Lydia Zakynthinou
We give a novel, unified derivation of conditional PAC-Bayesian and mutual information (MI) generalization bounds. We derive conditional MI bounds as an instance, with special choi…
Discovering outstanding subgroup lists for numeric targets using MDL
Hugo M. Proença, Peter Grünwald, Thomas Bäck +1
The task of subgroup discovery (SD) is to find interpretable descriptions of subsets of a dataset that stand out with respect to a target attribute. To address the problem of minin…
Minimum Description Length Revisited
Peter Grünwald, Teemu Roos
This is an up-to-date introduction to and overview of the Minimum Description Length (MDL) Principle, a theory of inductive inference that can be applied to general problems in sta…
Optional Stopping with Bayes Factors: a categorization and extension of folklore results, with an application to invariant situations
Allard Hendriksen, Rianne de Heide, Peter Grünwald
It is often claimed that Bayesian methods, in particular Bayes factor methods for hypothesis testing, can deal with optional stopping. We first give an overview, using elementary p…
A Tight Excess Risk Bound via a Unified PAC-Bayesian-Rademacher-Shtarkov-MDL Complexity
Peter D. Grünwald, Nishant A. Mehta
We present a novel notion of complexity that interpolates between and generalizes some classic existing complexity notions in learning theory: for estimators like empirical risk mi…