1 citations · 1 across the 11 of their papers we have counts for
7 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…
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…
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…