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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 101–150 of 265 papers

TitleStatusHype
Balanced off-policy evaluation in general action spacesCode0
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment SettingsCode0
Off-Policy Evaluation for Sequential Persuasion Process with Unobserved Confounding—0
Off-Policy Evaluation from Logged Human Feedback—0
Off-Policy Evaluation in Embedded Spaces—0
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders—0
Off-Policy Evaluation in Markov Decision Processes under Weak Distributional Overlap—0
Off-Policy Evaluation in Partially Observable Environments—0
Off-Policy Evaluation in Partially Observed Markov Decision Processes under Sequential Ignorability—0
Off-Policy Evaluation of Bandit Algorithm from Dependent Samples under Batch Update Policy—0
Off-Policy Evaluation of Probabilistic Identity Data in Lookalike Modeling—0
Off-Policy Evaluation of Slate Policies under Bayes Risk—0
Off-Policy Evaluation via Off-Policy Classification—0
Off-Policy Evaluation via the Regularized Lagrangian—0
Off-Policy Evaluation with Policy-Dependent Optimization Response—0
Off-Policy Exploitability-Evaluation in Two-Player Zero-Sum Markov Games—0
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory—0
Off-Policy Interval Estimation with Lipschitz Value Iteration—0
Off-Policy Risk Assessment in Contextual Bandits—0
Online Learning for Recommendations at Grubhub—0
On Minimax Optimal Offline Policy Evaluation—0
On the Curses of Future and History in Future-dependent Value Functions for Off-policy Evaluation—0
On the Sample Complexity of Vanilla Model-Based Offline Reinforcement Learning with Dependent Samples—0
On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation—0
OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators—0
Optimal discharge of patients from intensive care via a data-driven policy learning framework—0
Optimal Mixture Weights for Off-Policy Evaluation with Multiple Behavior Policies—0
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling—0
Practical Marginalized Importance Sampling with the Successor Representation—0
Primal-Dual Spectral Representation for Off-policy Evaluation—0
Privacy Preserving Off-Policy Evaluation—0
Probabilistic Offline Policy Ranking with Approximate Bayesian Computation—0
Quantile Off-Policy Evaluation via Deep Conditional Generative Learning—0
Reliable Off-policy Evaluation for Reinforcement Learning—0
RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation—0
Debiased Off-Policy Evaluation for Recommendation Systems—0
Safe Evaluation For Offline Learning: Are We Ready To Deploy?—0
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks—0
Sample Complexity of Preference-Based Nonparametric Off-Policy Evaluation with Deep Networks—0
Scalable and Robust Self-Learning for Skill Routing in Large-Scale Conversational AI Systems—0
Scalable and Safe Remediation of Defective Actions in Self-Learning Conversational Systems—0
Scaling Marginalized Importance Sampling to High-Dimensional State-Spaces via State Abstraction—0
Semi-gradient DICE for Offline Constrained Reinforcement Learning—0
STEEL: Singularity-aware Reinforcement Learning—0
Smoothed functional-based gradient algorithms for off-policy reinforcement learning: A non-asymptotic viewpoint—0
Stabilizing Temporal Difference Learning via Implicit Stochastic Recursion—0
Stateful Offline Contextual Policy Evaluation and Learning—0
Statistical Bootstrapping for Uncertainty Estimation in Off-Policy Evaluation—0
Statistical Estimation of Confounded Linear MDPs: An Instrumental Variable Approach—0
Statistically Efficient Variance Reduction with Double Policy Estimation for Off-Policy Evaluation in Sequence-Modeled Reinforcement Learning—0
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