Obtaining first-order regret bounds—regret bounds scaling not as the worst-case but with
some measure of the performance of the optimal policy on a given instance—is a core …

Best-of-both-worlds algorithms for online learning which achieve near-optimal regret in both
the adversarial and the stochastic regimes have received growing attention recently …

A recurring theme in statistical learning, online learning, and beyond is that faster
convergence rates are possible for problems with low noise, often quantified by the …

This paper presents multi-armed bandit (MAB) algorithms that work well in adversarial
environments and that offer improved performance by exploiting inherent structures in such …

We consider the adversarial linear contextual bandit setting, whichallows for the loss
functions associated with each of $ K $ arms to changeover time without restriction …

In this paper, we consider how to construct best-of-both-worlds linear bandit algorithms that
achieve nearly optimal performance for both stochastic and adversarial environments. For …

We introduce a modification of follow the regularised leader and combine it with the log
determinant potential and suitable loss estimators to prove that the minimax regret for …

In this paper, we prove that Distributional Reinforcement Learning (DistRL), which learns the
return distribution, can obtain second-order bounds in both online and offline RL in general …

This study aims to develop bandit algorithms that automatically exploit tendencies of certain
environments to improve performance, without any prior knowledge regarding the …

This paper offers a nearly optimal algorithm for online linear optimization with delayed
bandit feedback. Online linear optimization with bandit feedback, or nonstochastic linear …