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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 151–200 of 265 papers

TitleStatusHype
STITCH-OPE: Trajectory Stitching with Guided Diffusion for Off-Policy Evaluation—0
Task Selection Policies for Multitask Learning—0
Taylor Expansion Policy Optimization—0
Cramming Contextual Bandits for On-policy Statistical Evaluation—0
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation—0
Towards A Unified Policy Abstraction Theory and Representation Learning Approach in Markov Decision Processes—0
Towards Robust Off-Policy Evaluation via Human Inputs—0
Triply Robust Off-Policy Evaluation—0
Unbiased Offline Evaluation for Learning to Rank with Business Rules—0
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling—0
Variance-Aware Off-Policy Evaluation with Linear Function Approximation—0
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits—0
Weighted model estimation for offline model-based reinforcement learning—0
Why Should I Trust You, Bellman? Evaluating the Bellman Objective with Off-Policy Data—0
Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation—0
Accountable Off-Policy Evaluation via a Kernelized Bellman Statistics—0
Accountable Off-Policy Evaluation With Kernel Bellman Statistics—0
Adaptive Trade-Offs in Off-Policy Learning—0
A maximum-entropy approach to off-policy evaluation in average-reward MDPs—0
An Instrumental Variable Approach to Confounded Off-Policy Evaluation—0
A Practical Guide of Off-Policy Evaluation for Bandit Problems—0
A Principled Path to Fitted Distributional Evaluation—0
A Review of Off-Policy Evaluation in Reinforcement Learning—0
A Spectral Approach to Off-Policy Evaluation for POMDPs—0
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning—0
A Fast Convergence Theory for Offline Decision Making—0
A Unified Off-Policy Evaluation Approach for General Value Function—0
Automated Off-Policy Estimator Selection via Supervised Learning—0
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization—0
Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation—0
Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation—0
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces—0
Bellman Residual Orthogonalization for Offline Reinforcement Learning—0
Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions—0
Bootstrapping Fitted Q-Evaluation for Off-Policy Inference—0
Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation—0
CANDOR: Counterfactual ANnotated DOubly Robust Off-Policy Evaluation—0
Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains—0
Characterization of Efficient Influence Function for Off-Policy Evaluation Under Optimal Policies—0
CoinDICE: Off-Policy Confidence Interval Estimation—0
Combining Parametric and Nonparametric Models for Off-Policy Evaluation—0
Concept-driven Off Policy Evaluation—0
Confidence Interval for Off-Policy Evaluation from Dependent Samples via Bandit Algorithm: Approach from Standardized Martingales—0
Confident Natural Policy Gradient for Local Planning in q_π-realizable Constrained MDPs—0
Conformal Off-Policy Evaluation in Markov Decision Processes—0
Conformal Off-Policy Prediction in Contextual Bandits—0
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning—0
Consistent On-Line Off-Policy Evaluation—0
Counterfactual Analysis in Dynamic Latent State Models—0
Counterfactual Learning with General Data-generating Policies—0
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