2 citations · 5 across the 7 of their papers we have counts for
9 papers
When Statisticians Can Also Be AI Engineers, What Could We Build?
Ruoqing Zhu
Statistical software makes methods widely available. Good statistical work, however, often requires conversations that clarify the scientific question, how the data were collected,…
Offline Policy Evaluation via Mixed Bellman Residuals and Adaptive Critic Representations
Amitakshar Biswas, Yuhan Li, Ruoqing Zhu
Evaluating a target policy using data generated by a different behavior policy remains a fundamental challenge in reinforcement learning. While most existing work relies on the sta…
Vector-Valued Distributional Reinforcement Learning Policy Evaluation: A Hilbert Space Embedding Approach
Mehrdad Mohammadi, Qi Zheng, Ruoqing Zhu
We propose Kernel Embedding Distributional Reinforcement Learning (KE-DRL) an offline method to estimate kernel mean embeddings of conditional multivariate return distributions fro…
Reinforcement Learning with Continuous Actions Under Unmeasured Confounding
Yuhan Li, Eugene Han, Yifan Hu +4
This paper addresses the challenge of offline policy learning in reinforcement learning with continuous action spaces when unmeasured confounders are present. While most existing r…
Consistent Order Determination of Markov Decision Process
Chuyun Ye, Lixing Zhu, Ruoqing Zhu
The Markov assumption in Markov Decision Processes (MDPs) is fundamental in reinforcement learning, influencing both theoretical research and practical applications. Existing metho…
AI in Pharma for Personalized Sequential Decision-Making: Methods, Applications and Opportunities
Yuhan Li, Hongtao Zhang, Keaven Anderson +2
In the pharmaceutical industry, the use of artificial intelligence (AI) has seen consistent growth over the past decade. This rise is attributed to major advancements in statistica…
Stage-Aware Learning for Dynamic Treatments
Hanwen Ye, Wenzhuo Zhou, Ruoqing Zhu +1
Recent advances in dynamic treatment regimes (DTRs) facilitate the search for optimal treatments, which are tailored to individuals' specific needs and able to maximize their expec…
Quasi-optimal Reinforcement Learning with Continuous Actions
Yuhan Li, Wenzhuo Zhou, Ruoqing Zhu
Many real-world applications of reinforcement learning (RL) require making decisions in continuous action environments. In particular, determining the optimal dose level plays a vi…
Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks
Yifan Chen, Tianning Xu, Dilek Hakkani-Tur +3
Multiple sampling-based methods have been developed for approximating and accelerating node embedding aggregation in graph convolutional networks (GCNs) training. Among them, a lay…