Multi-armed bandit has been well-known for its efficiency in online decision-making in terms
of minimizing the loss of the participants' welfare during experiments (ie, the regret). In …

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

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 …

We study the multi-armed bandit problem with subGaussian rewards. The explore-then-
commit (ETC) strategy, which consists of an exploration phase followed by an exploitation …

We study the batched best arm identification (BBAI) problem, where the learner's goal is to
identify the best arm while switching the policy as less as possible. In particular, we aim to …

Crowdsourcing enables a paradigm to conduct the manual annotation and the analytics by
those recruited workers, with their rewards relevant to the quality of the results. Existing …

Sequential decision‐making involves making informed decisions based on continuous
interactions with a complex environment. This process is ubiquitous in various applications …

Recently, there has been extensive study of cooperative multi-agent multi-armed bandits
where a set of distributed agents cooperatively play the same multi-armed bandit game. The …

As mobile devices proliferate and mobile applications diversify, Mobile Edge Computing
(MEC) has become widely adopted to efficiently allocate computing resources at the network …

In this paper, we investigate the problem of pure exploration in the context of multi-armed
bandits, with a specific focus on scenarios where arms are pulled in fixed-size batches …