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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 101–125 of 265 papers

TitleStatusHype
A Review of Off-Policy Evaluation in Reinforcement Learning—0
Doubly Robust Kernel Statistics for Testing Distributional Treatment EffectsCode0
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
Anytime-valid off-policy inference for contextual banditsCode1
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model—0
A Policy-Guided Imitation Approach for Offline Reinforcement LearningCode1
Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Models—0
Towards Robust Off-Policy Evaluation via Human Inputs—0
Towards A Unified Policy Abstraction Theory and Representation Learning Approach in Markov Decision Processes—0
On the Reuse Bias in Off-Policy Reinforcement LearningCode0
Statistical Estimation of Confounded Linear MDPs: An Instrumental Variable Approach—0
Future-Dependent Value-Based Off-Policy Evaluation in POMDPsCode0
Conformal Off-policy PredictionCode0
Conformal Off-Policy Prediction in Contextual Bandits—0
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks—0
Markovian Interference in Experiments—0
Hybrid Value Estimation for Off-policy Evaluation and Offline Reinforcement Learning—0
Counterfactual Analysis in Dynamic Latent State Models—0
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