2 citations · 2 across the 6 of their papers we have counts for
8 papers
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…
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…
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…
Reproducible workflow for online AI in digital health
Susobhan Ghosh, Bhanu T. Gullapalli, Daiqi Gao +5
Online artificial intelligence (AI) algorithms are an important component of digital health interventions. These online algorithms are designed to continually learn and improve the…
Active Measuring in Reinforcement Learning With Delayed Negative Effects
Daiqi Gao, Ziping Xu, Aseel Rawashdeh +2
Measuring states in reinforcement learning (RL) can be costly in real-world settings and may negatively influence future outcomes. We introduce the Actively Observable Markov Decis…
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 and efficient use of data. Yet these advantages cre…
The Fallacy of Minimizing Cumulative Regret in the Sequential Task Setting
Ziping Xu, Kelly W. Zhang, Susan A. Murphy
Online Reinforcement Learning (RL) is typically framed as the process of minimizing cumulative regret (CR) through interactions with an unknown environment. However, real-world RL…
Online learning in bandits with predicted context
Yongyi Guo, Ziping Xu, Susan Murphy
We consider the contextual bandit problem where at each time, the agent only has access to a noisy version of the context and the error variance (or an estimator of this variance).…