63 citations · 115 across the 10 of their papers we have counts for
11 papers
Nearly Minimax Discrete Distribution Estimation in Kullback-Leibler Divergence with High Probability
Dirk van der Hoeven, Julia Olkhovskaia, Tim van Erven
We consider the fundamental problem of estimating a discrete distribution on a domain of size with high probability in Kullback-Leibler divergence. We provide upper and lower b…
Sample-efficient Learning of Concepts with Theoretical Guarantees: from Data to Concepts without Interventions
Hidde Fokkema, Tim van Erven, Sara Magliacane
Machine learning is a vital part of many real-world systems, but several concerns remain about the lack of interpretability, explainability and robustness of black-box AI systems.…
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits
Julia Olkhovskaya, Jack Mayo, Tim van Erven +2
We consider the adversarial linear contextual bandit setting, which allows for the loss functions associated with each of arms to change over time without restriction. Assuming…
Modifying Squint for Prediction with Expert Advice in a Changing Environment
Thom Neuteboom, Tim van Erven
We provide a new method for online learning, specifically prediction with expert advice, in a changing environment. In a non-changing environment the Squint algorithm has been desi…
Distributed Online Learning for Joint Regret with Communication Constraints
Dirk van der Hoeven, Hédi Hadiji, Tim van Erven
We consider distributed online learning for joint regret with communication constraints. In this setting, there are multiple agents that are connected in a graph. Each round, an ad…
MetaGrad: Adaptation using Multiple Learning Rates in Online Learning
Tim van Erven, Wouter M. Koolen, Dirk van der Hoeven
We provide a new adaptive method for online convex optimization, MetaGrad, that is robust to general convex losses but achieves faster rates for a broad class of special functions,…
Explaining Predictions by Approximating the Local Decision Boundary
Georgios Vlassopoulos, Tim van Erven, Henry Brighton +1
Constructing accurate model-agnostic explanations for opaque machine learning models remains a challenging task. Classification models for high-dimensional data, like images, are o…
The Many Faces of Exponential Weights in Online Learning
Dirk van der Hoeven, Tim van Erven, Wojciech Kotłowski
A standard introduction to online learning might place Online Gradient Descent at its center and then proceed to develop generalizations and extensions like Online Mirror Descent a…
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…
Second-order Quantile Methods for Experts and Combinatorial Games
Wouter M. Koolen, Tim van Erven
We aim to design strategies for sequential decision making that adjust to the difficulty of the learning problem. We study this question both in the setting of prediction with expe…
Catching Up Faster by Switching Sooner: A Prequential Solution to the AIC-BIC Dilemma
Tim van Erven, Peter Grunwald, Steven de Rooij
Bayesian model averaging, model selection and its approximations such as BIC are generally statistically consistent, but sometimes achieve slower rates og convergence than other me…