20 citations · 50 across the 47 of their papers we have counts for
6 papers · 2 filters
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…
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…
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…