39 citations · 55 across the 9 of their papers we have counts for
9 papers
Recurrent Event Analysis with Ordinary Differential Equations
Bo Meng, Weijing Tang, Gongjun Xu +1
This paper introduces a general framework for analyzing recurrent event data by modeling the conditional mean function of the recurrent event process as the solution to an Ordinary…
Minimax Regret Learning for Data with Heterogeneous Subgroups
Weibin Mo, Weijing Tang, Songkai Xue +2
Modern complex datasets often consist of various sub-populations with known group information. In the presence of sub-population heterogeneity, it is crucial to develop robust and…
Population-level Balance in Signed Networks
Weijing Tang, Ji Zhu
Statistical network models are useful for understanding the underlying formation mechanism and characteristics of complex networks. However, statistical models for \textit{signed n…
Incorporating External Risk Information with the Cox Model under Population Heterogeneity: Applications to Trans-Ancestry Polygenic Hazard Scores
Di Wang, Wen Ye, Ji Zhu +5
Polygenic hazard score (PHS) models designed for European ancestry (EUR) individuals provide ample information regarding survival risk discrimination. Incorporating such informatio…
KL-divergence Based Deep Learning for Discrete Time Model
Li Liu, Xiangeng Fang, Di Wang +2
Neural Network (Deep Learning) is a modern model in Artificial Intelligence and it has been exploited in Survival Analysis. Although several improvements have been shown by previou…
Survival Analysis via Ordinary Differential Equations
Weijing Tang, Kevin He, Gongjun Xu +1
This paper introduces an Ordinary Differential Equation (ODE) notion for survival analysis. The ODE notion not only provides a unified modeling framework, but more importantly, als…
SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks
Weijing Tang, Jiaqi Ma, Qiaozhu Mei +1
In this paper, we propose a flexible model for survival analysis using neural networks along with scalable optimization algorithms. One key technical challenge for directly applyin…
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model
Jiaqi Ma, Xinyang Yi, Weijing Tang +4
We investigate the Plackett-Luce (PL) model based listwise learning-to-rank (LTR) on data with partitioned preference, where a set of items are sliced into ordered and disjoint par…
A Flexible Generative Framework for Graph-based Semi-supervised Learning
Jiaqi Ma, Weijing Tang, Ji Zhu +1
We consider a family of problems that are concerned about making predictions for the majority of unlabeled, graph-structured data samples based on a small proportion of labeled sam…