3 citations · 6 across the 27 of their papers we have counts for
9 papers · 1 filter
Theoretical Foundations of @ Reinforcement Learning
Riccardo Poiani, Martino Bernasconi, Andrea Celli
Reinforcement Learning is a cornerstone technique for modern large reasoning models. Usually, for difficult tasks such as code generation and theorem proving, the agent is evaluate…
Online Learning in the Random Order Model
Martino Bernasconi, Andrea Celli, Riccardo Colini-Baldeschi +3
In the random-order model for online learning, the sequence of losses is chosen upfront by an adversary and presented to the learner after a random permutation. Any random-order in…
Non-Asymptotic Analysis of (Sticky) Track-and-Stop
Riccardo Poiani, Martino Bernasconi, Andrea Celli
In pure exploration problems, a statistician sequentially collects information to answer a question about some stochastic and unknown environment. The probability of returning a wr…
Pure Exploration with Infinite Answers
Riccardo Poiani, Martino Bernasconi, Andrea Celli
We study pure exploration problems in which the set of correct answers is possibly infinite. For example, such problems arise when regressing a continuous function on the means of…
Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information
Maria-Florina Balcan, Martino Bernasconi, Matteo Castiglioni +3
We study the problem of online learning in Stackelberg games with side information between a leader and a sequence of followers. In every round the leader observes contextual infor…
Beyond Primal-Dual Methods in Bandits with Stochastic and Adversarial Constraints
Martino Bernasconi, Matteo Castiglioni, Andrea Celli +1
We address a generalization of the bandit with knapsacks problem, where a learner aims to maximize rewards while satisfying an arbitrary set of long-term constraints. Our goal is t…
No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimization
Martino Bernasconi, Matteo Castiglioni, Andrea Celli
In the bandits with knapsacks framework (BwK) the learner has resource-consumption (packing) constraints. We focus on the generalization of BwK in which the learner has a set o…
Bandits with Replenishable Knapsacks: the Best of both Worlds
Martino Bernasconi, Matteo Castiglioni, Andrea Celli +1
The bandits with knapsack (BwK) framework models online decision-making problems in which an agent makes a sequence of decisions subject to resource consumption constraints. The tr…
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…