Skip to yearly menu bar Skip to main content


( events)   Timezone:  
Poster
Wed Dec 08 12:30 AM -- 02:00 AM (PST)
Control Variates for Slate Off-Policy Evaluation
Nikos Vlassis · Ashok Chandrashekar · Fernando Amat · Nathan Kallus

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 the self-normalized PI estimators. Experiments with real-world recommender data as well as synthetic data validate these improvements in practice.