11 papers
Decoupling Corruption and Horizon in Robust Contextual Pricing
Matteo Castiglioni, Francesco Emanuele Stradi
The paper proposes an online algorithm for repeated contextual pricing that tolerates a bounded number of corrupted sale feedbacks, achieving regret that scales with the corruption…
Beyond Slater's Condition in Online CMDPs with Stochastic and Adversarial Constraints
Francesco Emanuele Stradi, Eleonora Fidelia Chiefari, Matteo Castiglioni +2
The paper proposes a new online algorithm for episodic constrained Markov decision processes that achieves sublinear regret and constraint violation without assuming Slater's condi…
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…
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…