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Poster
ROI Maximization in Stochastic Online Decision-Making
Nicolò Cesa-Bianchi · Tom Cesari · Yishay Mansour · Vianney Perchet

Thu Dec 09 12:30 AM -- 02:00 AM (PST) @
in Poster Session 5 »
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 $\Pi$ at a rate of order $\min\big\{1/(N\Delta^2),N^{-1/3}\}$, where $N$ is the number of innovations and $\Delta$ is the suboptimality gap in $\Pi$. 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.
Keywords: Bandits · Online Learning

Author Information

Nicolò Cesa-Bianchi (Università degli Studi di Milano, Italy)
Tom Cesari (ANITI & TSE)
Yishay Mansour (Tel Aviv University & Google)
Vianney Perchet (ENSAE & Criteo AI Lab)

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