15 citations · 58 across the 17 of their papers we have counts for
17 papers
Distributional Shift-Aware Off-Policy Interval Estimation: A Unified Error Quantification Framework
Wenzhuo Zhou, Yuhan Li, Ruoqing Zhu +1
We study high-confidence off-policy evaluation in the context of infinite-horizon Markov decision processes, where the objective is to establish a confidence interval (CI) for the…
Policy Learning for Individualized Treatment Regimes on Infinite Time Horizon
Wenzhuo Zhou, Yuhan Li, Ruoqing Zhu
With the recent advancements of technology in facilitating real-time monitoring and data collection, "just-in-time" interventions can be delivered via mobile devices to achieve bot…
Corrected kernel principal component analysis for model structural change detection
Luoyao Yu, Lixing Zhu, Ruoqing Zhu +1
This paper develops a method to detect model structural changes by applying a Corrected Kernel Principal Component Analysis (CKPCA) to construct the so-called central distribution…
Confidence Band Estimation for Survival Random Forests
Sarah Elizabeth Formentini, Wei Liang, Ruoqing Zhu
Survival random forest is a popular machine learning tool for modeling censored survival data. However, there is currently no statistically valid and computationally feasible appro…
On Variance Estimation of Random Forests with Infinite-Order U-statistics
Tianning Xu, Ruoqing Zhu, Xiaofeng Shao
Infinite-order U-statistics (IOUS) has been used extensively on subbagging ensemble learning algorithms such as random forests to quantify its uncertainty. While normality results…
Random Forest Weighted Local Fréchet Regression with Random Objects
Rui Qiu, Zhou Yu, Ruoqing Zhu
Statistical analysis is increasingly confronted with complex data from metric spaces. Petersen and Müller (2019) established a general paradigm of Fréchet regression with complex m…
Estimating Optimal Infinite Horizon Dynamic Treatment Regimes via pT-Learning
Wenzhuo Zhou, Ruoqing Zhu, Annie Qu
Recent advances in mobile health (mHealth) technology provide an effective way to monitor individuals' health statuses and deliver just-in-time personalized interventions. However,…
Dermoscopic Image Classification with Neural Style Transfer
Yutong Li, Ruoqing Zhu, Annie Qu +1
Skin cancer, the most commonly found human malignancy, is primarily diagnosed visually via dermoscopic analysis, biopsy, and histopathological examination. However, unlike other ty…
Dimension Reduction Forests: Local Variable Importance using Structured Random Forests
Joshua Daniel Loyal, Ruoqing Zhu, Yifan Cui +1
Random forests are one of the most popular machine learning methods due to their accuracy and variable importance assessment. However, random forests only provide variable importan…
Estimating heterogeneous treatment effects with right-censored data via causal survival forests
Yifan Cui, Michael R. Kosorok, Erik Sverdrup +2
Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation. Building on this line of work, we introduce causal survival forests, which c…
orthoDr: Semiparametric Dimension Reduction via Orthogonality Constrained Optimization
Ruoqing Zhu, Jiyang Zhang, Ruilin Zhao +3
orthoDr is a package in R that solves dimension reduction problems using orthogonality constrained optimization approach. The package serves as a unified framework for many regress…
Constructing Stabilized Dynamic Treatment Regimes for Censored Data
Ying-Qi Zhao, Ruoqing Zhu, Guanhua Chen +1
Stabilized dynamic treatment regimes are sequential decision rules for individual patients that not only adaptive throughout the disease progression but also remain consistent over…
Semi-orthogonal Non-negative Matrix Factorization with an Application in Text Mining
Jack Yutong Li, Ruoqing Zhu, Annie Qu +2
Emergency Department (ED) crowding is a worldwide issue that affects the efficiency of hospital management and the quality of patient care. This occurs when the request for an admi…
A Parsimonious Personalized Dose Finding Model via Dimension Reduction
Wenzhuo Zhou, Ruoqing Zhu, Donglin Zeng
Learning an individualized dose rule in personalized medicine is a challenging statistical problem. Existing methods often suffer from the curse of dimensionality, especially when…
Tree based weighted learning for estimating individualized treatment rules with censored data
Yifan Cui, Ruoqing Zhu, Michael Kosorok
Estimating individualized treatment rules is a central task for personalized medicine. [zhao2012estimating] and [zhang2012robust] proposed outcome weighted learning to estimate ind…
Consistency of survival tree and forest models: splitting bias and correction
Yifan Cui, Ruoqing Zhu, Mai Zhou +1
Random survival forest and survival trees are popular models in statistics and machine learning. However, there is a lack of general understanding regarding consistency, splitting…
Counting Process Based Dimension Reduction Methods for Censored Outcomes
Qiang Sun, Ruoqing Zhu, Tao Wang +1
We propose a class of dimension reduction methods for right censored survival data using a counting process representation of the failure process. Semiparametric estimating equatio…