1.6k citations · 1.8k across the 18 of their papers we have counts for
6 papers · 1 filter
Generalization Guarantees via Algorithm-dependent Rademacher Complexity
Sarah Sachs, Tim van Erven, Liam Hodgkinson +2
Algorithm- and data-dependent generalization bounds are required to explain the generalization behavior of modern machine learning algorithms. In this context, there exists informa…
Adaptive Selective Sampling for Online Prediction with Experts
Rui M. Castro, Fredrik Hellström, Tim van Erven
We consider online prediction of a binary sequence with expert advice. For this setting, we devise label-efficient forecasting algorithms, which use a selective sampling scheme tha…
Attribution-based Explanations that Provide Recourse Cannot be Robust
Hidde Fokkema, Rianne de Heide, Tim van Erven
Different users of machine learning methods require different explanations, depending on their goals. To make machine learning accountable to society, one important goal is to get…
PAC-Bayes Mini-tutorial: A Continuous Union Bound
Tim van Erven
When I first encountered PAC-Bayesian concentration inequalities they seemed to me to be rather disconnected from good old-fashioned results like Hoeffding's and Bernstein's inequa…
A Second-order Bound with Excess Losses
Pierre Gaillard, Gilles Stoltz, Tim Van Erven
We study online aggregation of the predictions of experts, and first show new second-order regret bounds in the standard setting, which are obtained via a version of the Prod algor…
Adaptive Hedge
Tim van Erven, Peter Grünwald, Wouter M. Koolen +1
Most methods for decision-theoretic online learning are based on the Hedge algorithm, which takes a parameter called the learning rate. In most previous analyses the learning rate…