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20202026
most citedSingle-Sample Prophet Inequalities via Greedy-Ordered Selection

25 citations · 121 across the 26 of their papers we have counts for

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cs.LG2023★ 1 cited

Bandits with Replenishable Knapsacks: the Best of both Worlds

Martino Bernasconi, Matteo Castiglioni, Andrea Celli +1

The bandits with knapsack (BwK) framework models online decision-making problems in which an agent makes a sequence of decisions subject to resource consumption constraints. The tr…

cs.LG2023★ 1 cited

Fairness in Streaming Submodular Maximization over a Matroid Constraint

Marwa El Halabi, Federico Fusco, Ashkan Norouzi-Fard +2

Streaming submodular maximization is a natural model for the task of selecting a representative subset from a large-scale dataset. If datapoints have sensitive attributes such as g…

cs.LG2023★ 3 cited

Repeated Bilateral Trade Against a Smoothed Adversary

Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni +2

We study repeated bilateral trade where an adaptive -smooth adversary generates the valuations of sellers and buyers. We provide a complete characterization of the regret regime…

cs.LG2022

Learning on the Edge: Online Learning with Stochastic Feedback Graphs

Emmanuel Esposito, Federico Fusco, Dirk van der Hoeven +1

The framework of feedback graphs is a generalization of sequential decision-making with bandit or full information feedback. In this work, we study an extension where the directed…

cs.LG2021★ 1 cited

Beyond Bandit Feedback in Online Multiclass Classification

Dirk van der Hoeven, Federico Fusco, Nicolò Cesa-Bianchi

We study the problem of online multiclass classification in a setting where the learner's feedback is determined by an arbitrary directed graph. While including bandit feedback as…

cs.LG2021★ 8 cited

A Regret Analysis of Bilateral Trade

Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni +2

Bilateral trade, a fundamental topic in economics, models the problem of intermediating between two strategic agents, a seller and a buyer, willing to trade a good for which they h…