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Off-policy evaluation

Off-policy Evaluation (OPE), or offline evaluation in general, evaluates the performance of hypothetical policies leveraging only offline log data. It is particularly useful in applications where the online interaction involves high stakes and expensive setting such as precision medicine and recommender systems.

Papers

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Showing 121–130 of 265 papers

TitleStatusHype
Low Variance Off-policy Evaluation with State-based Importance SamplingCode0
Counterfactual Learning with General Data-generating Policies—0
Offline Policy Evaluation and Optimization under Confounding—0
Policy-Adaptive Estimator Selection for Off-Policy EvaluationCode0
Counterfactual Learning with Multioutput Deep KernelsCode0
Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation—0
Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions—0
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous ActionsCode0
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model—0
Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Models—0
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