4 papers
An Improved Algorithm for Adversarial Linear Contextual Bandits via Reduction
Tim van Erven, Jack Mayo, Julia Olkhovskaya +1
We present an oracle-efficient, near-optimal algorithm for linear contextual bandits with adversarial losses and stochastic action sets, only requiring a linear optimization oracle…
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…
Accelerated Rates between Stochastic and Adversarial Online Convex Optimization
Sarah Sachs, Hedi Hadiji, Tim van Erven +1
Stochastic and adversarial data are two widely studied settings in online learning. But many optimization tasks are neither i.i.d. nor fully adversarial, which makes it of fundamen…
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.…