SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 91–100 of 265 papers

TitleStatusHype
Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning—0
Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning—0
Efron-Stein PAC-Bayesian Inequalities—0
Emphatic TD Bellman Operator is a Contraction—0
Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment—0
Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian Optimization Perspective—0
Generalized Emphatic Temporal Difference Learning: Bias-Variance Analysis—0
Expected Sarsa(λ) with Control Variate for Variance Reduction—0
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency—0
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare—0
Show:102550
← PrevPage 10 of 27Next →

No leaderboard results yet.