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cs.LG2026
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…
cs.LG2025
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…
cs.LG2024
An Online Feasible Point Method for Benign Generalized Nash Equilibrium Problems
Sarah Sachs, Hedi Hadiji, Tim van Erven +1
We consider a repeatedly played generalized Nash equilibrium game. This induces a multi-agent online learning problem with joint constraints. An important challenge in this setting…