This paper considers the sample complexity of the multi-armed bandit with dependencies
among the arms. Some of the most successful algorithms for this problem use the principle …

This paper considers the use of a simple posterior sampling algorithm to balance between
exploration and exploitation when learning to optimize actions such as in multiarmed bandit …

We consider a generalization of stochastic bandits where the set of arms, X, is allowed to be
a generic measurable space and the mean-payoff function is “locally Lipschitz” with respect …

This paper introduces and addresses a wide class of stochastic bandit problems where the
function mapping the arm to the corresponding reward exhibits some known structural …

We consider stochastic multi-armed bandits where the expected reward is a unimodal
function over partially ordered arms. This important class of problems has been recently …

We consider the problem of efficiently exploring the arms of a stochastic bandit to identify the
best subset. Under the PAC and the fixed-budget formulations, we derive improved bounds …

We study the effects of approximate inference on the performance of Thompson sampling in
the $ k $-armed bandit problems. Thompson sampling is a successful algorithm for online …

We present a new strategy for gap estimation in randomized algorithms for multiarmed
bandits and combine it with the EXP3++ algorithm of Seldin and Slivkins (2014). In the …

The multi-armed bandit problem is a popular model for studying exploration/exploitation
trade-off in sequential decision problems. Many algorithms are now available for this well …

The online multi-armed bandit problem and its generalizations are repeated decision
making problems, where the goal is to select one of several possible decisions in every …