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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 126–150 of 265 papers

TitleStatusHype
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
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
Scalable and Robust Self-Learning for Skill Routing in Large-Scale Conversational AI Systems—0
Off-Policy Evaluation with Online Adaptation for Robot Exploration in Challenging Environments—0
Model-Free and Model-Based Policy Evaluation when Causality is UncertainCode0
Marginalized Operators for Off-policy Reinforcement Learning—0
Bellman Residual Orthogonalization for Offline Reinforcement Learning—0
Off-Policy Evaluation in Embedded Spaces—0
Off-Policy Evaluation with Policy-Dependent Optimization Response—0
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided MarketsCode0
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory—0
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