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 …

We study a general class of online learning problems where the feedback is specified by a
graph. This class includes online prediction with expert advice and the multi-armed bandit …

We consider the problem of preference bandits in the contextual setting. At each round, the
learner is presented with a context set of $ K $ items, chosen randomly from a potentially …

We study networks of communicating learning agents that cooperate to solve a common
nonstochastic bandit problem. Agents use an underlying communication network to get …

This study considers online learning with general directed feedback graphs. For this
problem, we present best-of-both-worlds algorithms that achieve nearly tight regret bounds …

We study repeated bilateral trade where an adaptive $\sigma $-smooth adversary generates
the valuations of sellers and buyers. We provide a complete characterization of the regret …

We study the problem of $ K $-armed dueling bandit for both stochastic and adversarial
environments, where the goal of the learner is to aggregate information through relative …

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

Partial monitoring is a rich framework for sequential decision making under uncertainty that
generalizes many well known bandit models, including linear, combinatorial and dueling …

Bilateral trade models the problem of intermediating between two rational agents—a seller
and a buyer—both characterized by a private valuation for an item they want to trade. We …