Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

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 …

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

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 …

This paper introduces and addresses a wide class of stochastic bandit problems where the
function mapping the arm to the corresponding reward exhibits some known structural …

We study the multi-armed bandit problem with multiple plays and a budget constraint for
both the stochastic and the adversarial setting. At each round, exactly K out of N possible …

We propose the contextual combinatorial cascading bandits, a combinatorial online learning
game, where at each time step a learning agent is given a set of contextual information, then …

Consider a queueing system consisting of multiple servers. Jobs arrive over time and enter a
queue for service; the goal is to minimize the size of this queue. At each opportunity for …