arXiv:2405.15332
공식 코드 · Papers with Code
Cross-Validated Off-Policy Evaluation
원문 초록
We study estimator selection and hyper-parameter tuning in off-policy evaluation. Although cross-validation is the most popular method for model selection in supervised learning, off-policy evaluation relies mostly on theory, which provides only limited guidance to practitioners. We show how to use cross-validation for off-policy evaluation. This challenges a popular belief that cross-validation in off-policy evaluation is not feasible. We evaluate our method empirically and show that it addresses a variety of use cases.
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Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian Optimization Perspective
Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale
Leveraging Predictive Equivalence in Decision Trees
The use of cross validation in the analysis of designed experiments
Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement Learning
토론
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