This paper studies two variants of the best arm identification (BAI) problem under the
streaming model, where we have a stream of n arms with reward distributions supported on …

We consider the following problem in this paper: given a set of n distributions, find the top-m
ones with the largest means. This problem is also called top-m arm identifications in the …

We study high-dimensional sparse estimation under three natural constraints:
communication constraints, local privacy constraints, and linear measurements …

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 …

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

In this paper, we study the tradeoffs between the time speedup and the round complexity in
the collaborative learning model with non-IID data, where multiple agents interact with …

We consider the classic problem of $(\epsilon,\delta) $-\texttt {PAC} learning a best arm
where the goal is to identify with confidence $1-\delta $ an arm whose mean is an $\epsilon …

We study the effect of reward variance heterogeneity in the approximate top-$ m $ arm
identification setting. In this setting, the reward for the $ i $-th arm follows a $\sigma^ 2_i …

We study the problem of coarse ranking in the multi-armed bandits (MAB) setting, where we
have a set of arms each of which is associated with an unknown distribution. The task is to …