Abstract The Multi-Armed Bandits (MAB) framework highlights the trade-off between
acquiring new knowledge (Exploration) and leveraging available knowledge (Exploitation) …

This paper is in the field of stochastic Multi-Armed Bandits (MABs), ie, those sequential
selection techniques able to learn online using only the feedback given by the chosen …

Motivated by recommendation problems in music streaming platforms, we propose a
nonstationary stochastic bandit model in which the expected reward of an arm depends on …

In stochastic multi-armed bandits, the reward distribution of each arm is assumed to be
stationary. This assumption is often violated in practice (eg, in recommendation systems) …

Abstract The Combined Algorithm Selection and Hyperparameter optimization (CASH) is
one of the most fundamental problems in Automatic Machine Learning (AutoML). The …

We study the recovering bandits problem, a variant of the stochastic multi-armed bandit
problem where the expected reward of each arm varies according to some unknown …

A common assumption in recommender systems (RS) is the existence of a best fixed
recommendation strategy. Such strategy may be simple and work at the item level (eg, in …

Psychological research shows that enjoyment of many goods is subject to satiation, with
short-term satisfaction declining after repeated exposures to the same item. Nevertheless …

In many application domains (eg, recommender systems, intelligent tutoring systems), the
rewards associated to the available actions tend to decrease over time. This decay is either …

In the route choice problem, self-interested drivers aim at choosing routes that minimise
travel costs between their origins and destinations. We model this problem as a multiagent …