activity
20172023
most citedTree based weighted learning for estimating individualized treatment rules with censored data

5 citations · 6 across the 7 of their papers we have counts for

collaborators

10 papers

stat.ML2023

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…

stat.ME20231 cited

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…

stat.ME2023

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…

stat.ME2022

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…

eess.IV2021

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…

stat.ME2021

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…