20 citations · 145 across the 58 of their papers we have counts for
9 papers · 1 filter
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…
A Unifying Framework for Online Optimization with Long-Term Constraints
Matteo Castiglioni, Andrea Celli, Alberto Marchesi +2
We study online learning problems in which a decision maker has to take a sequence of decisions subject to long-term constraints. The goal of the decision maker is to maximize…
Sequential Information Design: Learning to Persuade in the Dark
Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi +2
We study a repeated information design problem faced by an informed sender who tries to influence the behavior of a self-interested receiver. We consider settings where the receive…
Multi-Armed Bandit Problem with Temporally-Partitioned Rewards: When Partial Feedback Counts
Giulia Romano, Andrea Agostini, Francesco Trovò +2
There is a rising interest in industrial online applications where data becomes available sequentially. Inspired by the recommendation of playlists to users where their preferences…