1 citations · 1 across the 16 of their papers we have counts for
18 papers
Truly Adapting to Adversarial Constraints in Constrained MABs
Francesco Emanuele Stradi, Kalana Kalupahana, Matteo Castiglioni +2
We study the constrained variant of the \emph{multi-armed bandit} (MAB) problem, in which the learner aims not only at minimizing the total loss incurred during the learning dynami…
Beyond Slater's Condition in Online CMDPs with Stochastic and Adversarial Constraints
Francesco Emanuele Stradi, Eleonora Fidelia Chiefari, Matteo Castiglioni +2
We study \emph{online episodic Constrained Markov Decision Processes} (CMDPs) under both stochastic and adversarial constraints. We provide a novel algorithm whose guarantees great…
No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need!
Francesco Emanuele Stradi, Matteo Castiglioni, Alberto Marchesi +2
We study online decision making problems under resource constraints, where both reward and cost functions are drawn from distributions that may change adversarially over time. We f…
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…
Online Bayesian Persuasion Without a Clue
Francesco Bacchiocchi, Matteo Bollini, Matteo Castiglioni +2
We study online Bayesian persuasion problems in which an informed sender repeatedly faces a receiver with the goal of influencing their behavior through the provision of payoff-rel…
Contracting With a Reinforcement Learning Agent by Playing Trick or Treat
Matteo Bollini, Francesco Bacchiocchi, Matteo Castiglioni +2
We study principal-agent problems where a farsighted agent takes costly actions in an MDP. The core challenge in these settings is that agent's actions are hidden to the principal,…
Optimal Strong Regret and Violation in Constrained MDPs via Policy Optimization
Francesco Emanuele Stradi, Matteo Castiglioni, Alberto Marchesi +1
We study online learning in \emph{constrained MDPs} (CMDPs), focusing on the goal of attaining sublinear strong regret and strong cumulative constraint violation. Differently from…
Best-of-Both-Worlds Policy Optimization for CMDPs with Bandit Feedback
Francesco Emanuele Stradi, Anna Lunghi, Matteo Castiglioni +2
We study online learning in constrained Markov decision processes (CMDPs) in which rewards and constraints may be either stochastic or adversarial. In such settings, Stradi et al.(…
A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints
Francesco Emanuele Stradi, Filippo Cipriani, Lorenzo Ciampiconi +3
We address the challenging problem of dynamically pricing complementary items that are sequentially displayed to customers. An illustrative example is the online sale of flight tic…
The Sample Complexity of Stackelberg Games
Francesco Bacchiocchi, Matteo Bollini, Matteo Castiglioni +2
Stackelberg games (SGs) constitute the most fundamental and acclaimed models of strategic interactions involving some form of commitment. Moreover, they form the basis of more elab…
Learning Constrained Markov Decision Processes With Non-stationary Rewards and Constraints
Francesco Emanuele Stradi, Anna Lunghi, Matteo Castiglioni +2
In constrained Markov decision processes (CMDPs) with adversarial rewards and constraints, a well-known impossibility result prevents any algorithm from attaining both sublinear re…
Markov Persuasion Processes: Learning to Persuade from Scratch
Francesco Bacchiocchi, Francesco Emanuele Stradi, Matteo Castiglioni +2
In Bayesian persuasion, an informed sender strategically discloses information to a receiver so as to persuade them to undertake desirable actions. Recently, a growing attention ha…
Learning Adversarial MDPs with Stochastic Hard Constraints
Francesco Emanuele Stradi, Matteo Castiglioni, Alberto Marchesi +1
We study online learning in constrained Markov decision processes (CMDPs) with adversarial losses and stochastic hard constraints, under bandit feedback. We consider three scenario…
Multi-Agent Contract Design beyond Binary Actions
Federico Cacciamani, Martino Bernasconi, Matteo Castiglioni +1
We study hidden-action principal-agent problems with multiple agents. Unlike previous work, we consider a general setting in which each agent has an arbitrary number of actions, an…
Learning Optimal Contracts: How to Exploit Small Action Spaces
Francesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi +1
We study principal-agent problems in which a principal commits to an outcome-dependent payment scheme -- called contract -- in order to induce an agent to take a costly, unobservab…
Steering No-Regret Learners to a Desired Equilibrium
Brian Hu Zhang, Gabriele Farina, Ioannis Anagnostides +7
A mediator observes no-regret learners playing an extensive-form game repeatedly across rounds. The mediator attempts to steer players toward some desirable predetermined equil…
Optimal Rates and Efficient Algorithms for Online Bayesian Persuasion
Martino Bernasconi, Matteo Castiglioni, Andrea Celli +3
Bayesian persuasion studies how an informed sender should influence beliefs of rational receivers who take decisions through Bayesian updating of a common prior. We focus on the on…