Bayesian Counterfactual Risk Minimization


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.

International Conference on Machine Learning (ICML)