Data di Pubblicazione:
2021
Citazione:
ROI Maximization in Stochastic Online Decision-Making / N. Cesa Bianchi, T. Cesari, Y. Mansour, V. Perchet (ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS). - In: Advances in Neural Information Processing Systems / [a cura di] M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, J. Wortman Vaughan. - [s.l] : Curran Associates, 2021. - ISBN 9781713845393. - pp. 9152-9166 (( Intervento presentato al 34. convegno Neural Information Processing Systems tenutosi a virtual nel 2021.
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.
Tipologia IRIS:
03 - Contributo in volume
Elenco autori:
N. Cesa Bianchi, T. Cesari, Y. Mansour, V. Perchet
Link alla scheda completa:
Link al Full Text:
Titolo del libro:
Advances in Neural Information Processing Systems