Learning from repeated play in a fixed two-player zero-sum game is a classic problem in
game theory and online learning. We consider a variant of this problem where the game …

This work addresses the problem of regret minimization in non-stochastic multi-armed bandit
problems, focusing on performance guarantees that hold with high probability. Such results …

Algorithms for online learning typically require one or more boundedness assumptions: that
the domain is bounded, that the losses are Lipschitz, or both. In this paper, we develop a …

Most of the literature on learning in games has focused on the restrictive setting where the
underlying repeated game does not change over time. Much less is known about the …

We study the classic online learning problem of predicting with expert advice, and propose a
truly parameter-free and adaptive algorithm that achieves several objectives simultaneously …

Recently, there has been a growing research interest in the analysis of dynamic regret,
which measures the performance of an online learner against a sequence of local …

To cope with changing environments, recent developments in online learning have
introduced the concepts of adaptive regret and dynamic regret independently. In this paper …

We study reinforcement learning in episodic inhomogeneous MDPs with adversarial full-
information rewards and the unknown transition kernel. We consider the linear mixture …

We investigate online convex optimization in non-stationary environments and choose
dynamic regret as the performance measure, defined as the difference between cumulative …

We resolve the long-standing" impossible tuning" issue for the classic expert problem and
show that, it is in fact possible to achieve regret $ O\left (\sqrt {(\ln d)\sum_t\ell_ {t, i}^ 2}\right) …