69 citations · 231 across the 22 of their papers we have counts for
22 papers
Robust Low-rank Tensor Decomposition with the Criterion
Qiang Heng, Eric C. Chi, Yufeng Liu
The growing prevalence of tensor data, or multiway arrays, in science and engineering applications motivates the need for tensor decompositions that are robust against outliers. In…
Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules
Weibin Mo, Zhengling Qi, Yufeng Liu
We thank the opportunity offered by editors for this discussion and the discussants for their insightful comments and thoughtful contributions. We also want to congratulate Kallus…
Efficient Learning of Optimal Individualized Treatment Rules for Heteroscedastic or Misspecified Treatment-Free Effect Models
Weibin Mo, Yufeng Liu
Recent development in data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and outco…
Angle-based hierarchical classification using exact label embedding
Yiwei Fan, Xiaoling Lu, Yufeng Liu +1
Hierarchical classification problems are commonly seen in practice. However, most existing methods do not fully utilize the hierarchical information among class labels. In this pap…
Learning Optimal Distributionally Robust Individualized Treatment Rules
Weibin Mo, Zhengling Qi, Yufeng Liu
Recent development in the data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and o…
High dimensional precision medicine from patient-derived xenografts
Naim U. Rashid, Daniel J. Luckett, Jingxiang Chen +8
The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers potential to improve patient outcomes by leveraging th…
Statistical Analysis of Stationary Solutions of Coupled Nonconvex Nonsmooth Empirical Risk Minimization
Zhengling Qi, Ying Cui, Yufeng Liu +1
This paper has two main goals: (a) establish several statistical properties---consistency, asymptotic distributions, and convergence rates---of stationary solutions and values of a…
Estimation of Individualized Decision Rules Based on an Optimized Covariate-Dependent Equivalent of Random Outcomes
Zhengling Qi, Ying Cui, Yufeng Liu +1
Recent exploration of optimal individualized decision rules (IDRs) for patients in precision medicine has attracted a lot of attention due to the heterogeneous responses of patient…
Penalized linear regression with high-dimensional pairwise screening
Siliang Gong, Kai Zhang, Yufeng Liu
In variable selection, most existing screening methods focus on marginal effects and ignore dependence between covariates. To improve the performance of selection, we incorporate p…
Double Sparsity Kernel Learning with Automatic Variable Selection and Data Extraction
Jingxiang Chen, Chong Zhang, Michael R. Kosorok +1
Learning with Reproducing Kernel Hilbert Spaces (RKHS) has been widely used in many scientific disciplines. Because a RKHS can be very flexible, it is common to impose a regulariza…
Efficient Test-based Variable Selection for High-dimensional Linear Models
Siliang Gong, Kai Zhang, Yufeng Liu
Variable selection plays a fundamental role in high-dimensional data analysis. Various methods have been developed for variable selection in recent years. Well-known examples are f…
Estimating Individualized Treatment Rules for Ordinal Treatments
Jingxiang Chen, Haoda Fu, Xuanyao He +2
Precision medicine is an emerging scientific topic for disease treatment and prevention that takes into account individual patient characteristics. It is an important direction for…
Stability Enhanced Large-Margin Classifier Selection
Will Wei Sun, Guang Cheng, Yufeng Liu
Stability is an important aspect of a classification procedure because unstable predictions can potentially reduce users' trust in a classification system and also harm the reprodu…
Simultaneous Clustering and Estimation of Heterogeneous Graphical Models
Botao Hao, Will Wei Sun, Yufeng Liu +1
We consider joint estimation of multiple graphical models arising from heterogeneous and high-dimensional observations. Unlike most previous approaches which assume that the cluste…
Joint Estimation of Multiple Dependent Gaussian Graphical Models with Applications to Mouse Genomics
Yuying Xie, Yufeng Liu, William Valdar
Gaussian graphical models are widely used to represent conditional dependence among random variables. In this paper, we propose a novel estimator for data arising from a group of G…
Large-Margin Classification with Multiple Decision Rules
Patrick K. Kimes, D. Neil Hayes, J. S. Marron +1
Binary classification is a common statistical learning problem in which a model is estimated on a set of covariates for some outcome indicating the membership of one of two classes…
Statistical Significance for Hierarchical Clustering
Patrick K. Kimes, Yufeng Liu, D. Neil Hayes +1
Cluster analysis has proved to be an invaluable tool for the exploratory and unsupervised analysis of high dimensional datasets. Among methods for clustering, hierarchical approach…
Significance Analysis for Pairwise Variable Selection in Classification
Xingye Qiao, Yufeng Liu, J. S. Marron
The goal of this article is to select important variables that can distinguish one class of data from another. A marginal variable selection method ranks the marginal effects for c…
IsoDOT Detects Differential RNA-isoform Expression/Usage with respect to a Categorical or Continuous Covariate with High Sensitivity and Specificity
Wei Sun, Yufeng Liu, James J. Crowley +14
We have developed a statistical method named IsoDOT to assess differential isoform expression (DIE) and differential isoform usage (DIU) using RNA-seq data. Here isoform usage refe…
Statistical Significance of Clustering using Soft Thresholding
Hanwen Huang, Yufeng Liu, Ming Yuan +1
Clustering methods have led to a number of important discoveries in bioinformatics and beyond. A major challenge in their use is determining which clusters represent important unde…
Local kernel canonical correlation analysis with application to virtual drug screening
Daniel Samarov, J. S. Marron, Yufeng Liu +2
Drug discovery is the process of identifying compounds which have potentially meaningful biological activity. A major challenge that arises is that the number of compounds to searc…
Variable selection for the multicategory SVM via adaptive sup-norm regularization
Hao Helen Zhang, Yufeng Liu, Yichao Wu +1
The Support Vector Machine (SVM) is a popular classification paradigm in machine learning and has achieved great success in real applications. However, the standard SVM can not sel…