20 citations · 69 across the 50 of their papers we have counts for
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Optimal Regret for Online Market Making with Limit Order Book
Maria Elena Vischi, Francesco Emanuele Stradi, Alberto Marchesi
We study online learning in market making, where, at each round, a market maker posts bid and ask prices before observing the market price and the private valuation of an incoming…
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…
Online Resource Allocation with Replenishable Budgets
Eleonora Fidelia Chiefari, Francesco Emanuele Stradi, Alberto Marchesi
Online Resource Allocation (ORA) is a fundamental framework for sequential decision-making problems under budget constraints. Classical ORA models typically assume that resources a…
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…
Online Resource Allocation With General Constraints
Eleonora Fidelia Chiefari, Francesco Emanuele Stradi, Matteo Castiglioni +1
Online resource allocation (ORA) is a fundamental framework for sequential decision-making problems under budget constraints, with applications ranging from online advertising to r…
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…
Toward Optimal Regret in Robust Pricing: Decoupling Corruption and Time
Kalana Kalupahana, Francesco Emanuele Stradi, Matteo Castiglioni +1
We design the first regret guarantees for robust dynamic pricing that decouple the dependence on the corruption and the time horizon . In dynamic pricing, a seller with unli…
Multi-Armed Bandits With Best-Action Queries
Francesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi +1
We study \emph{multi-armed bandits} (MABs) augmented with \emph{best-action queries}, in which the learner may additionally query an oracle that reveals the best arm in the current…
Replicable Constrained Bandits
Matteo Bollini, Gianmarco Genalti, Francesco Emanuele Stradi +2
Algorithmic \emph{replicability} has recently been introduced to address the need for reproducible experiments in machine learning. A \emph{replicable online learning} algorithm is…
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…
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…
Learning in Bayesian Stackelberg Games With Unknown Follower's Types
Matteo Bollini, Francesco Bacchiocchi, Samuel Coutts +2
We study online learning in Bayesian Stackelberg games, where a leader repeatedly interacts with a follower whose unknown private type is independently drawn at each round from an…