10 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…
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…
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…
-Adaptive Regret Minimization in Heavy-Tailed Bandits
Gianmarco Genalti, Lupo Marsigli, Nicola Gatti +1
Heavy-tailed distributions naturally arise in several settings, from finance to telecommunications. While regret minimization under subgaussian or bounded rewards has been widely s…
A Best-of-Both-Worlds Algorithm for Constrained MDPs with Long-Term Constraints
Jacopo Germano, Francesco Emanuele Stradi, Gianmarco Genalti +3
We study online learning in episodic constrained Markov decision processes (CMDPs), where the learner aims at collecting as much reward as possible over the episodes, while satisfy…
Autoregressive Bandits
Francesco Bacchiocchi, Gianmarco Genalti, Davide Maran +4
Autoregressive processes naturally arise in a large variety of real-world scenarios, including stock markets, sales forecasting, weather prediction, advertising, and pricing. When…
Dynamic Pricing with Volume Discounts in Online Settings
Marco Mussi, Gianmarco Genalti, Alessandro Nuara +3
According to the main international reports, more pervasive industrial and business-process automation, thanks to machine learning and advanced analytic tools, will unlock more tha…