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

An abundance of recent impossibility results establish that regret minimization in Markov
games with adversarial opponents is both statistically and computationally intractable …

We consider the best-of-both-worlds problem for learning an episodic Markov Decision
Process through $ T $ episodes, with the goal of achieving $\widetilde {\mathcal {O}}(\sqrt …

This work studies the problem of learning episodic Markov Decision Processes with known
transition and bandit feedback. We develop the first algorithm with a``best-of-both …

Adaptivity to the difficulties of a problem is a key property in sequential decision-making
problems to broaden the applicability of algorithms. Follow-the-regularized-leader (FTRL) …

We develop a new approach to obtaining high probability regret bounds for online learning
with bandit feedback against an adaptive adversary. While existing approaches all require …

We study the online learning with feedback graphs framework introduced by Mannor and
Shamir (2011), in which the feedback received by the online learner is specified by a graph …

We study the stochastic shortest path problem with adversarial costs and known transition,
and show that the minimax regret is $ O (\sqrt {DT_\star K}) $ and $ O (\sqrt {DT_\star SA K}) …

Conversational recommender systems (CRSs) have been proposed recently to mitigate the
cold-start problem suffered by the traditional recommender systems. By introducing …

We consider the problem of combining and learning over a set of adversarial bandit
algorithms with the goal of adaptively tracking the best one on the fly. The Corral algorithm of …