activity
20172021
most citedNear-optimal Individualized Treatment Recommendations

3 citations · 7 across the 4 of their papers we have counts for

collaborators

9 papers

stat.ML2021

Query-augmented Active Metric Learning

Yujia Deng, Yubai Yuan, Haoda Fu +1

In this paper we propose an active metric learning method for clustering with pairwise constraints. The proposed method actively queries the label of informative instance pairs, wh…

stat.ME2021

Model-Assisted Uniformly Honest Inference for Optimal Treatment Regimes in High Dimension

Yunan Wu, Lan Wang, Haoda Fu

This paper develops new tools to quantify uncertainty in optimal decision making and to gain insight into which variables one should collect information about given the potential c…

stat.ML20203 cited

Near-optimal Individualized Treatment Recommendations

Haomiao Meng, Ying-Qi Zhao, Haoda Fu +1

Individualized treatment recommendation (ITR) is an important analytic framework for precision medicine. The goal is to assign proper treatments to patients based on their individu…

stat.ML20202 cited

Boosting Algorithms for Estimating Optimal Individualized Treatment Rules

Duzhe Wang, Haoda Fu, Po-Ling Loh

We present nonparametric algorithms for estimating optimal individualized treatment rules. The proposed algorithms are based on the XGBoost algorithm, which is known as one of the…

stat.ME20201 cited

Multicategory Angle-based Learning for Estimating Optimal Dynamic Treatment Regimes with Censored Data

Fei Xue, Yanqing Zhang, Wenzhuo Zhou +2

An optimal dynamic treatment regime (DTR) consists of a sequence of decision rules in maximizing long-term benefits, which is applicable for chronic diseases such as HIV infection…

stat.ME2018

Quantile Regression Modeling of Recurrent Event Risk

Huijuan Ma, Limin Peng, Chiung-Yu Huang +1

Progression of chronic disease is often manifested by repeated occurrences of disease-related events over time. Delineating the heterogeneity in the risk of such recurrent events c…