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 …

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 …

An adaptive learning system aims at providing instruction tailored to the current status of a
learner, differing from the traditional classroom experience. The latest advances in …

Reinforcement learning (RL) algorithms have been long recognized as powerful tools for
optimal sequential decision making. The framework is concerned with a decision maker, the …

The Gittins scheduling policy minimizes the mean response in the single-server M/G/1
queue in a wide variety of settings. Most famously, Gittins is optimal when preemption is …

Multi-armed bandit problems pertain to optimal sequential decision making and learning in
unknown environments. Since the first bandit problem posed by Thompson in 1933 for the …

Learning preferences implicit in the choices humans make is a well studied problem in both
economics and computer science. However, most work makes the assumption that humans …

Queueing delays are ubiquitous in many domains, including computer systems, service
systems, communication networks, supply chains, and transportation. Queueing and …

Restless bandits are a class of sequential resource allocation problems concerned with
allocating one or more resources among several alternative processes where the evolution …

Abstract We study the Stochastic Multi-armed Bandit problem under bounded arm-memory.
In this setting, the arms arrive in a stream, and the number of arms that can be stored in the …