ROI Maximization in Stochastic Online Decision-Making

Nicolò Cesa-Bianchi, Tommaso Cesari, Yishay Mansour, Vianney Perchet

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We introduce a novel theoretical framework for Return On Investment (ROI) maximization in repeated decision-making. Our setting is motivated by the use case of companies that regularly receive proposals for technological innovations and want to quickly decide whether they are worth implementing. We design an algorithm for learning ROI-maximizing decision-making policies over a sequence of innovation proposals. Our algorithm provably converges to an optimal policy in class Π at a rate of order min {1/(N ∆2), N−1/3}, where N is the number of innovations and ∆ is the suboptimality gap in Π. A significant hurdle of our formulation, which sets it aside from other online learning problems such as bandits, is that running a policy does not provide an unbiased estimate of its performance.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021
EditorsMarc'Aurelio Ranzato, Alina Beygelzimer, Yann Dauphin, Percy S. Liang, Jenn Wortman Vaughan
PublisherNeural information processing systems foundation
Pages9152-9166
Number of pages15
ISBN (Electronic)9781713845393
StatePublished - 2021
Event35th Conference on Neural Information Processing Systems, NeurIPS 2021 - Virtual, Online
Duration: 6 Dec 202114 Dec 2021

Publication series

NameAdvances in Neural Information Processing Systems
Volume11
ISSN (Print)1049-5258

Conference

Conference35th Conference on Neural Information Processing Systems, NeurIPS 2021
CityVirtual, Online
Period6/12/2114/12/21

Funding

FundersFunder number
EU Horizon 2020 ICT-48 research and innovation action951847, ANR-19-PI3A-0004
European Union’sHorizon 2020 research and innovation program882396
LabEx LMHANR-19-CE23-0026
Yandex Initiative for Machine LearningANR-11-LABX-0056-LMH
European Commission
European Cooperation in Science and TechnologyCA16228
Ministero dell’Istruzione, dell’Università e della Ricerca
Israel Science Foundation993/17
Tel Aviv University

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