Timezone: »

 
Poster
Control Variates for Slate Off-Policy Evaluation
Nikos Vlassis · Ashok Chandrashekar · Fernando Amat · Nathan Kallus

Wed Dec 08 12:30 AM -- 02:00 AM (PST) @

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.

Author Information

Nikos Vlassis (Netflix)
Ashok Chandrashekar (Warner Media)
Fernando Amat (Netflix)
Nathan Kallus (Cornell University)

More from the Same Authors