8 papers
An Efficient Minimax-Optimal Algorithm for Adversarial -Set Bandits
Francesco Bacchiocchi, Tommaso Cesari, Roberto Colomboni
We study adversarial combinatorial bandits with -set actions, where at each round the learner selects out of items and observes only the aggregate loss of the selected i…
No Extra Signals Needed: The Uniform Price of Explainable Information Design
Francesco Bacchiocchi, Tommaso Cesari, Roberto Colomboni
In information design, an informed sender aims to influence a receiver's decision by committing to a signaling scheme. However, optimal signaling schemes often rely on randomizatio…
Multi-Armed Bandits With Best-Action Queries
Francesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi +1
We study \emph{multi-armed bandits} (MABs) augmented with \emph{best-action queries}, in which the learner may additionally query an oracle that reveals the best arm in the current…
Learning in Bayesian Stackelberg Games With Unknown Follower's Types
Matteo Bollini, Francesco Bacchiocchi, Samuel Coutts +2
We study online learning in Bayesian Stackelberg games, where a leader repeatedly interacts with a follower whose unknown private type is independently drawn at each round from an…
Regret Minimization for Piecewise Linear Rewards: Contracts, Auctions, and Beyond
Francesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi +1
Most microeconomic models of interest involve optimizing a piecewise linear function. These include contract design in hidden-action principal-agent problems, selling an item in po…
Contract Design Under Approximate Best Responses
Francesco Bacchiocchi, Jiarui Gan, Matteo Castiglioni +2
Principal-agent problems model scenarios where a principal incentivizes an agent to take costly, unobservable actions through the provision of payments. Such problems are ubiquitou…