Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

We develop a novel and generic algorithm for the adversarial multi-armed bandit problem
(or more generally the combinatorial semi-bandit problem). When instantiated differently, our …

An online reinforcement learning algorithm is anytime if it does not need to know in advance
the horizon T of the experiment. A well-known technique to obtain an anytime algorithm from …

Motivated by cognitive radio networks, we consider the stochastic multiplayer multi-armed
bandit problem, where several players pull arms simultaneously and collisions occur if one …

Abstract We propose $\epsilon $-Exploring Thompson Sampling ($\epsilon $-TS), a
modified version of the Thompson Sampling (TS) algorithm for multi-armed bandits. In …

There has been increasing interest in applying multi-armed bandits to adaptive designs in
clinical trials. However, most literature assumes that a previous patient's survival response of …

We investigate stochastic combinatorial multi-armed bandit with semi-bandit feedback
(CMAB). In CMAB, the question of the existence of an efficient policy with an optimal …

Online auctions are one of the most fundamental facets of the modern economy and power
an industry generating hundreds of billions of dollars a year in revenue. Auction theory has …

We study the regret of Thompson sampling (TS) algorithms for exponential family bandits,
where the reward distribution is from a one-dimensional exponential family, which covers …

Thompson sampling is one of the most widely used algorithms in many online decision
problems due to its simplicity for implementation and superior empirical performance over …