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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 51–100 of 265 papers

TitleStatusHype
Off-policy evaluation for slate recommendationCode0
Off-Policy Evaluation with Out-of-Sample GuaranteesCode0
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision ProcessesCode0
Counterfactual-Augmented Importance Sampling for Semi-Offline Policy EvaluationCode0
Model-Free and Model-Based Policy Evaluation when Causality is UncertainCode0
Off-policy Evaluation in Doubly Inhomogeneous EnvironmentsCode0
Robust Offline Reinforcement learning with Heavy-Tailed RewardsCode0
Counterfactual Learning with Multioutput Deep KernelsCode0
Long-term Off-Policy Evaluation and LearningCode0
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal ModelsCode0
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous ActionsCode0
Cross-Validated Off-Policy EvaluationCode0
Low Variance Off-policy Evaluation with State-based Importance SamplingCode0
Learning Action Embeddings for Off-Policy EvaluationCode0
Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL PoliciesCode0
Leveraging Factored Action Spaces for Off-Policy EvaluationCode0
Marginal Density Ratio for Off-Policy Evaluation in Contextual BanditsCode0
Harnessing Distribution Ratio Estimators for Learning Agents with Quality and DiversityCode0
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy EvaluationCode0
Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement LearningCode0
Deeply-Debiased Off-Policy Interval EstimationCode0
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement LearningCode0
K-Nearest-Neighbor Resampling for Off-Policy Evaluation in Stochastic ControlCode0
Distributional Off-Policy Evaluation for Slate RecommendationsCode0
Distributional Off-policy Evaluation with Bellman Residual MinimizationCode0
Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal InterferencesCode0
DOLCE: Decomposing Off-Policy Evaluation/Learning into Lagged and Current EffectsCode0
Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and LearningCode0
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment SettingsCode0
Abstract Reward Processes: Leveraging State Abstraction for Consistent Off-Policy EvaluationCode0
Human Choice Prediction in Language-based Persuasion Games: Simulation-based Off-Policy EvaluationCode0
From Importance Sampling to Doubly Robust Policy GradientCode0
Doubly Robust Estimator for Off-Policy Evaluation with Large Action SpacesCode0
Doubly Robust Kernel Statistics for Testing Distributional Treatment EffectsCode0
Future-Dependent Value-Based Off-Policy Evaluation in POMDPsCode0
RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health InterventionsCode0
A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision ProcessesCode0
Off-policy Evaluation with Deeply-abstracted StatesCode0
Hallucinated Adversarial Control for Conservative Offline Policy EvaluationCode0
Off-Policy Evaluation Using Information Borrowing and Context-Based SwitchingCode0
Importance Sampling Policy Evaluation with an Estimated Behavior PolicyCode0
On the Reuse Bias in Off-Policy Reinforcement LearningCode0
Policy-Adaptive Estimator Selection for Off-Policy EvaluationCode0
Post Reinforcement Learning InferenceCode0
Conformal Off-policy PredictionCode0
Minimum Empirical Divergence for Sub-Gaussian Linear BanditsCode0
Safe Exploration for Optimizing Contextual BanditsCode0
Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service—0
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces—0
Counterfactual Learning with General Data-generating Policies—0
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