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Bayesian Counterfactual Risk Minimization
Ben London · Ted Sandler

Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #113
We present a Bayesian view of counterfactual risk minimization (CRM) for offline learning from logged bandit feedback. Using PAC-Bayesian analysis, we derive a new generalization bound for the truncated inverse propensity score estimator. We apply the bound to a class of Bayesian policies, which motivates a novel, potentially data-dependent, regularization technique for CRM. Experimental results indicate that this technique outperforms standard $L_2$ regularization, and that it is competitive with variance regularization while being both simpler to implement and more computationally efficient.

Author Information

Ben London (Amazon)
Ted Sandler (Amazon.com)

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