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 26–50 of 265 papers

TitleStatusHype
Adaptive Estimator Selection for Off-Policy EvaluationCode0
Balanced off-policy evaluation in general action spacesCode0
Batch Stationary Distribution EstimationCode0
Future-Dependent Value-Based Off-Policy Evaluation in POMDPsCode0
Off-policy Evaluation with Deeply-abstracted StatesCode0
Conformal Off-policy PredictionCode0
From Importance Sampling to Doubly Robust Policy GradientCode0
Hallucinated Adversarial Control for Conservative Offline Policy EvaluationCode0
Importance Sampling Policy Evaluation with an Estimated Behavior PolicyCode0
Doubly Robust Kernel Statistics for Testing Distributional Treatment EffectsCode0
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision ProcessesCode0
A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision ProcessesCode0
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy EvaluationCode0
Distributional Off-policy Evaluation with Bellman Residual MinimizationCode0
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment SettingsCode0
Deeply-Debiased Off-Policy Interval EstimationCode0
Distributional Off-Policy Evaluation for Slate RecommendationsCode0
DOLCE: Decomposing Off-Policy Evaluation/Learning into Lagged and Current EffectsCode0
Doubly robust off-policy evaluation with shrinkageCode0
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance WeightingCode0
Doubly Robust Estimator for Off-Policy Evaluation with Large Action SpacesCode0
Counterfactual Mean EmbeddingsCode0
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided MarketsCode0
Balanced Off-Policy Evaluation for Personalized PricingCode0
Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal InterferencesCode0
Show:102550
← PrevPage 2 of 11Next →

No leaderboard results yet.