Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

The multi-armed bandit is an important framework for balancing exploration with exploitation
in recommendation. Exploitation recommends content (eg, products, movies, music playlists) …

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

A stochastic combinatorial semi-bandit is an online learning problem where at each step a
learning agent chooses a subset of ground items subject to constraints, and then observes …

A search engine usually outputs a list of K web pages. The user examines this list, from the
first web page to the last, and chooses the first attractive page. This model of user behavior …

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 …

We study the {\em combinatorial pure exploration (CPE)} problem in the stochastic multi-
armed bandit setting, where a learner explores a set of arms with the objective of identifying …

We study the application of the Thompson sampling (TS) methodology to the stochastic
combinatorial multi-armed bandit (CMAB) framework. We analyze the standard TS algorithm …

This paper investigates stochastic and adversarial combinatorial multi-armed bandit
problems. In the stochastic setting under semi-bandit feedback, we derive a problem-specific …

In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework
that allows a general nonlinear reward function, whose expected value may not depend only …