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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 76–100 of 265 papers

TitleStatusHype
Leveraging Factored Action Spaces for Off-Policy EvaluationCode0
Off-Policy Evaluation of Ranking Policies under Diverse User BehaviorCode1
Off-policy Evaluation in Doubly Inhomogeneous EnvironmentsCode0
K-Nearest-Neighbor Resampling for Off-Policy Evaluation in Stochastic ControlCode0
Counterfactual Evaluation of Peer-Review Assignment PoliciesCode0
Scalable and Safe Remediation of Defective Actions in Self-Learning Conversational Systems—0
Human Choice Prediction in Language-based Persuasion Games: Simulation-based Off-Policy EvaluationCode0
Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling—0
Learning Action Embeddings for Off-Policy EvaluationCode0
Conformal Off-Policy Evaluation in Markov Decision Processes—0
On the Sample Complexity of Vanilla Model-Based Offline Reinforcement Learning with Dependent Samples—0
Hallucinated Adversarial Control for Conservative Offline Policy EvaluationCode0
Balanced Off-Policy Evaluation for Personalized PricingCode0
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare—0
Post Reinforcement Learning InferenceCode0
STEEL: Singularity-aware Reinforcement Learning—0
Variational Latent Branching Model for Off-Policy EvaluationCode0
Off-Policy Evaluation for Action-Dependent Non-Stationary EnvironmentsCode0
Off-Policy Evaluation with Out-of-Sample GuaranteesCode0
Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves—0
An Instrumental Variable Approach to Confounded Off-Policy Evaluation—0
Quantile Off-Policy Evaluation via Deep Conditional Generative Learning—0
Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information—0
Safe Evaluation For Offline Learning: Are We Ready To Deploy?—0
Scaling Marginalized Importance Sampling to High-Dimensional State-Spaces via State Abstraction—0
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