6 papers
Parameter-Free Heavy-Tailed Bandits
Gianmarco Genalti, Alberto Maria Metelli
Heavy-tailed distributions arise naturally in sequential decision-making problems such as financial investment, online advertising, and network management, where rare but extreme o…
Bridging Rested and Restless Bandits with Graph-Triggering: Rising and Rotting
Gianmarco Genalti, Marco Mussi, Nicola Gatti +3
Rested and Restless Bandits are two well-known bandit settings that are useful to model real-world sequential decision-making problems in which the expected reward of an arm evolve…
Online Packet Scheduling with Deadlines and Learning
Gianmarco Genalti, Achraf Azize, Vianney Perchet
Network routers that enforce Quality-of-Service (QoS) guarantees must decide, at every clock cycle, which expiring packet of information to transmit, even when the value of the pac…
Replicable Constrained Bandits
Matteo Bollini, Gianmarco Genalti, Francesco Emanuele Stradi +2
Algorithmic \emph{replicability} has recently been introduced to address the need for reproducible experiments in machine learning. A \emph{replicable online learning} algorithm is…
Data-Dependent Regret Bounds for Constrained MABs
Gianmarco Genalti, Francesco Emanuele Stradi, Matteo Castiglioni +2
This paper initiates the study of data-dependent regret bounds in constrained MAB settings. These bounds depend on the sequence of losses that characterize the problem instance. Th…
Catoni-Style Change Point Detection for Regret Minimization in Non-Stationary Heavy-Tailed Bandits
Gianmarco Genalti, Sujay Bhatt, Nicola Gatti +1
Regret minimization in stochastic non-stationary bandits gained popularity over the last decade, as it can model a broad class of real-world problems, from advertising to recommend…