20 citations · 145 across the 58 of their papers we have counts for
14 papers · 1 filter
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…
A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving
Luca Carminati, Federico Cacciamani, Marco Ciccone +1
\emph{Ex ante} correlation is becoming the mainstream approach for \emph{sequential adversarial team games}, where a team of players faces another team in a zero-sum game. It is kn…
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…