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arXiv · 1806.11500

Bayesian Counterfactual Risk Minimization

Ben London·Ted Sandler
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Abstract

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.

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Ben London, Ted Sandler. 2018-06-29. Bayesian Counterfactual Risk Minimization. https://arxiv.org/abs/1806.11500

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