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cs.LG2026
Uncertainty-Aware Selection of Online Algorithms with Simulator Ensembles
Yongyi Guo, Zifan Xu, Ziping Xu +1
The performance of online reinforcement learning depends critically on design choices, especially those that affect exploration. These choices are often selected by fitting a simul…
stat.ML2026
Statistical Inference for Misspecified Contextual Bandits
Yongyi Guo, Ziping Xu
Contextual bandit algorithms have transformed modern experimentation by enabling real-time adaptation for personalized treatment. Yet these advantages create challenges for statist…
stat.AP2026
Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions
Asim H. Gazi, Yongyi Guo, Daiqi Gao +3
Reinforcement learning (RL) has achieved remarkable success in real-world decision-making across diverse domains, including gaming, robotics, online advertising, public health, and…