1 citations · 1 across the 7 of their papers we have counts for
7 papers
Efficient First-Order Methods for Estimating Generalized Additive Index Models
Ziyu Peng, Linglingzhi Zhu, Yao Xie
Generalized additive index models (GAIMs) offer a flexible semiparametric framework for capturing complex data relationships, balancing the interpretability of parametric models wi…
CoreFlow: Low-Rank Matrix Generative Models
Dongze Wu, Linglingzhi Zhu, Yao Xie
Learning matrix-valued distributions from high-dimensional and possibly incomplete training data is challenging: ambient-space generative modeling is computationally expensive and…
Primal-Dual Methods for Nonsmooth Nonconvex Optimization with Orthogonality Constraints
Linglingzhi Zhu, Wentao Ding, Shangyuan Liu +1
Recent advancements in data science have significantly elevated the importance of orthogonally constrained optimization problems. The Riemannian approach has become a popular techn…
Dynamic Proximal Gradient Algorithms for Schatten- Quasi-Norm Regularized Problems
Weiping Shen, Linglingzhi Zhu, Yaohua Hu +2
This paper investigates numerical solution methods for the Schatten- quasi-norm regularized problem with , which has been widely studied for finding low-rank soluti…
Worst-case generation via minimax optimization in Wasserstein space
Xiuyuan Cheng, Yao Xie, Linglingzhi Zhu +1
Worst-case generation plays a critical role in evaluating robustness and stress-testing systems under distribution shifts, in applications ranging from machine learning models to p…
Beyond Maximum Likelihood: Variational Inequality Estimation for Generalized Linear Models
Linglingzhi Zhu, Jonghyeok Lee, Yao Xie
Generalized linear models (GLMs) are fundamental tools for statistical modeling, with maximum likelihood estimation (MLE) serving as the classical approach for parameter inference.…