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