1 citations · 1 across the 8 of their papers we have counts for
9 papers
Support-Dependent Regret Rates for Profit Maximization in Multilateral Trade
Francesco Bacchiocchi, Matteo Castiglioni, Anna Lunghi +1
We study online profit maximization in multilateral trade, a setting that generalizes both dynamic pricing and bilateral trade. At each round, an intermediary posts prices to buyer…
Breaking the Barrier for Regret Minimization With Bi-Dimensional CDFs
Matteo Castiglioni, Anna Lunghi, Alberto Marchesi
We study regret minimization for learning CDF-related objectives of the form \[ g(x)\cdot\mathbb{P}_{X\sim\mathcal{D}}(X\le x), \] over , where is a known Lipschitz fu…
Regret Minimization in Bilateral Trade With Perturbed Markets
Anna Lunghi, Matteo Castiglioni, Alberto Marchesi
We address the problem of maximizing Gain from Trade (GFT) in repeated buyer-seller exchanges subject to global budget balance constraints. While this problem is well-understood in…
A Stronger Benchmark for Online Bilateral Trade: From Fixed Prices to Distributions
Anna Lunghi, Mattia Piccinato, Matteo Castiglioni +1
We study online bilateral trade, where a learner facilitates repeated exchanges between a buyer and a seller to maximize the Gain From Trade (GFT), i.e., the social welfare. In doi…
The Sample Complexity of Uniform Approximation for Multi-Dimensional CDFs and Fixed-Price Mechanisms
Matteo Castiglioni, Anna Lunghi, Alberto Marchesi
We study the sample complexity of learning a uniform approximation of an -dimensional cumulative distribution function (CDF) within an error , when observations are restri…
Better Regret Rates in Bilateral Trade via Sublinear Budget Violation
Anna Lunghi, Matteo Castiglioni, Alberto Marchesi
Bilateral trade is a central problem in algorithmic economics, and recent work has explored how to design trading mechanisms using no-regret learning algorithms. However, no-regret…
Online Two-Sided Markets: Many Buyers Enhance Learning
Anna Lunghi, Matteo Castiglioni, Alberto Marchesi
We study a repeated trading problem in which a mechanism designer facilitates trade between a single seller and multiple buyers. Our model generalizes the classic bilateral trade s…
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.(…
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…