In machine learning, the notion of multi-armed bandits refers to a class of online learning
problems, in which an agent is supposed to simultaneously explore and exploit a given set …

We study the $ K $-armed contextual dueling bandit problem, a sequential decision making
setting in which the learner uses contextual information to make two decisions, but only …

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 …

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 …

With the recent advances of conversational recommendations, the recommender system is
able to actively and dynamically elicit user preference via conversational interactions. To …

We consider the regret minimization task in a dueling bandits problem with context
information. In every round of the sequential decision problem, the learner makes a context …

The K-armed dueling bandit problem, where the feedback is in the form of noisy pairwise
comparisons, has been widely studied. Previous works have only focused on the sequential …

We consider the problem of reward maximization in the dueling bandit setup along with
constraints on resource consumption. As in the classic dueling bandits, at each round the …

There has been much interest in recent years in the problem of dueling bandits, where on
each round the learner plays a pair of arms and receives as feedback the outcome of a …

We study the problem of non-stationary dueling bandits and provide the first adaptive
dynamic regret algorithm for this problem. The only two existing attempts in this line of work …