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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 151–175 of 265 papers

TitleStatusHype
Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making—0
On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation—0
Off-Policy Evaluation Using Information Borrowing and Context-Based SwitchingCode0
Optimal discharge of patients from intensive care via a data-driven policy learning framework—0
Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and LearningCode0
Weighted model estimation for offline model-based reinforcement learning—0
Loss Functions for Discrete Contextual Pricing with Observational Data—0
A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision ProcessesCode0
SOPE: Spectrum of Off-Policy EstimatorsCode0
Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision ProcessesCode0
Towards Hyperparameter-free Policy Selection for Offline Reinforcement LearningCode0
Off-Policy Evaluation in Partially Observed Markov Decision Processes under Sequential Ignorability—0
Stateful Offline Contextual Policy Evaluation and Learning—0
Why Should I Trust You, Bellman? Evaluating the Bellman Objective with Off-Policy Data—0
A Spectral Approach to Off-Policy Evaluation for POMDPs—0
Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation—0
Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service—0
State Relevance for Off-Policy EvaluationCode0
Debiasing Samples from Online Learning Using Bootstrap—0
Online Learning for Recommendations at Grubhub—0
A Unified Off-Policy Evaluation Approach for General Value Function—0
Supervised Off-Policy RankingCode0
Variance-Aware Off-Policy Evaluation with Linear Function Approximation—0
Control Variates for Slate Off-Policy EvaluationCode0
Robust Generalization despite Distribution Shift via Minimum Discriminating InformationCode0
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