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 derive an alternative proof for the regret of Thompson sampling (TS) in the stochastic
linear bandit setting. While we obtain a regret bound of order $ O (d^ 3/2\sqrtT) $ as in …

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 …

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

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 …

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

We study the online influence maximization problem in social networks under the
independent cascade model. Specifically, we aim to learn the set of" best influencers" in a …

Most recommender systems recommend a list of items. The user examines the list, from the
first item to the last, and often chooses the first attractive item and does not examine the rest …