Demystifying the Paradox of Importance Sampling with an Estimated History-Dependent Behavior Policy in Off-Policy Evaluation

Published in International Conference on Machine Learning (ICML), 2025

Recommended citation: Hongyi Zhou, Josiah Hanna, Jin Zhu, Ying Yang, Chengchun Shi. (2025). Demystifying the Paradox of Importance Sampling with an Estimated History-Dependent Behavior Policy in Off-Policy Evaluation. International Conference on Machine Learning (ICML).
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