Opportunistic spectrum access (OSA) has been regarded as the most promising approach to
solve the paradox between spectrum scarcity and waste. Intelligent decision making is key …

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 define a general framework for a large class of combinatorial multi-armed bandit (CMAB)
problems, where simple arms with unknown istributions form\em super arms. In each round …

In 1989 the first edition of this book set out Gittins' pioneering index solution to the multi-
armed bandit problem and his subsequent investigation of a wide of sequential resource …

The multi-armed bandit (MAB) model has been widely adopted for studying many practical
optimization problems (network resource allocation, ad placement, crowdsourcing, etc.) with …

We consider a non-Bayesian infinite horizon version of the multi-armed bandit problem with
the objective of designing simple policies whose regret increases slowly with time. In their …

We formulate the following combinatorial multi-armed bandit (MAB) problem: There are N
random variables with unknown mean that are each instantiated in an iid fashion over time …

We consider an assortment optimization problem where a retailer chooses an assortment of
products that maximizes the profit subject to a capacity constraint. The demand is …

In the past few years, differential privacy has become a standard concept in the area of
privacy. One of the most important problems in this field is to answer queries while …

This paper discusses the use of portfolio approaches based on bandit algorithms to optimize
multicriteria decision-making in recommender systems (accuracy and diversity). While …