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• W2810113936 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." @default.
• W2810113936 created "2018-07-10" @default.
• W2810113936 creator A5047338645 @default.
• W2810113936 creator A5054561921 @default.
• W2810113936 date "2018-06-29" @default.
• W2810113936 modified "2023-09-27" @default.
• W2810113936 title "Bayesian Counterfactual Risk Minimization" @default.
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