Control Variates for Slate Off-Policy Evaluation

• URL: http://arxiv.org/abs/2106.07914v1
• Date: Tue, 15 Jun 2021 06:59:53 GMT
• Title: Control Variates for Slate Off-Policy Evaluation
• Authors: Nikos Vlassis, Ashok Chandrashekar, Fernando Amat Gil, Nathan Kallus
• Abstract summary: We study the problem of off-policy evaluation from batched contextual bandit data with multidimensional actions. We obtain new estimators with risk improvement guarantees over both the PI and self-normalized PI estimators.
• Score: 112.35528337130118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the problem of off-policy evaluation from batched contextual bandit data with multidimensional actions, often termed slates. The problem is common to recommender systems and user-interface optimization, and it is particularly challenging because of the combinatorially-sized action space. Swaminathan et al. (2017) have proposed the pseudoinverse (PI) estimator under the assumption that the conditional mean rewards are additive in actions. Using control variates, we consider a large class of unbiased estimators that includes as specific cases the PI estimator and (asymptotically) its self-normalized variant. By optimizing over this class, we obtain new estimators with risk improvement guarantees over both the PI and self-normalized PI estimators. Experiments with real-world recommender data as well as synthetic data validate these improvements in practice.

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