136 citations · 816 across the 52 of their papers we have counts for
7 papers · 1 filter
Robust subgroup discovery
Hugo Manuel Proença, Peter Grünwald, Thomas Bäck +1
We introduce the problem of robust subgroup discovery, i.e., finding a set of interpretable descriptions of subsets that 1) stand out with respect to one or more target attributes,…
PAC-Bayes Un-Expected Bernstein Inequality
Zakaria Mhammedi, Peter D. Grunwald, Benjamin Guedj
We present a new PAC-Bayesian generalization bound. Standard bounds contain a $\sqrt{L_n \cdot \KL/n}$ complexity term which dominates unless , the empirical error of the lear…
Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning
Wouter M. Koolen, Peter Grünwald, Tim van Erven
We consider online learning algorithms that guarantee worst-case regret rates in adversarial environments (so they can be deployed safely and will perform robustly), yet adapt opti…
Fast Rates for General Unbounded Loss Functions: from ERM to Generalized Bayes
Peter D. Grünwald, Nishant A. Mehta
We present new excess risk bounds for general unbounded loss functions including log loss and squared loss, where the distribution of the losses may be heavy-tailed. The bounds hol…
Fast rates in statistical and online learning
Tim van Erven, Peter D. Grünwald, Nishant A. Mehta +2
The speed with which a learning algorithm converges as it is presented with more data is a central problem in machine learning --- a fast rate of convergence means less data is nee…
Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families
Peter Bartlett, Peter Grunwald, Peter Harremoes +2
We study online learning under logarithmic loss with regular parametric models. Hedayati and Bartlett (2012b) showed that a Bayesian prediction strategy with Jeffreys prior and seq…
Follow the Leader If You Can, Hedge If You Must
Steven de Rooij, Tim van Erven, Peter D. Grünwald +1
Follow-the-Leader (FTL) is an intuitive sequential prediction strategy that guarantees constant regret in the stochastic setting, but has terrible performance for worst-case data.…