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 …

During online decision making in Multi-Armed Bandits (MAB), one needs to conduct
inference on the true mean reward of each arm based on data collected so far at each step …

Adaptive and sequential experiment design is a well-studied area in numerous domains. We
survey and synthesize the work of the online statistical learning paradigm referred to as multi …

Context Delivering faster value to customers with online experimentation is an emerging
practice in industry. Multi-Armed Bandit (MAB) based experiments have the potential to …

It is well known that in stochastic multi-armed bandits (MAB), the sample mean of an arm is
typically not an unbiased estimator of its true mean. In this paper, we decouple three …

We study the structure of regret-minimizing policies in the {\em many-armed} Bayesian multi-
armed bandit problem: in particular, with $ k $ the number of arms and $ T $ the time …

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 …

Regret minimization in streaming multi-armed bandits (MABs) has been studied extensively,
and recent work has shown that algorithms with $ o (K) $ memory have to incur $\Omega …

A key feature of sequential decision making under uncertainty is a need to balance between
exploiting—choosing the best action according to the current knowledge, and exploring …

In this paper, we propose a Minimax Concave Penalized Multi-Armed Bandit (MCP-Bandit)
algorithm for a decision-maker facing high-dimensional data with latent sparse structure in …