2 citations · 3 across the 5 of their papers we have counts for
7 papers
Maximizing Modular plus Non-monotone Submodular Functions
Xin Sun, Chenchen Wu, Dachuan Xu +1
The research problem in this work is the relaxation of maximizing non-negative submodular plus modular with the entire real number domain as its value range over a family of down-c…
An improved approximation algorithm for maximizing a DR-submodular function over a convex set
Donglei Du, Zhicheng Liu, Chenchen Wu +2
Maximizing a DR-submodular function subject to a general convex set is an NP-hard problem arising from many applications in combinatorial optimization and machine learning. While i…
Approximate the individually fair k-center with outliers
Lu Han, Dachuan Xu, Yicheng Xu +1
In this paper, we propose and investigate the individually fair -center with outliers (IFCO). In the IFCO, we are given an -sized vertex set in a metric space, as well…
Outliers Detection Is Not So Hard: Approximation Algorithms for Robust Clustering Problems Using Local Search Techniques
Yishui Wang, Rolf H. Möhring, Chenchen Wu +2
In this paper, we consider two types of robust models of the -median/-means problems: the outlier-version (-MedO/-MeaO) and the penalty-version (-MedP/-MeaP), in…
Eigenvalue-corrected Natural Gradient Based on a New Approximation
Kai-Xin Gao, Xiao-Lei Liu, Zheng-Hai Huang +5
Using second-order optimization methods for training deep neural networks (DNNs) has attracted many researchers. A recently proposed method, Eigenvalue-corrected Kronecker Factoriz…
A Trace-restricted Kronecker-Factored Approximation to Natural Gradient
Kai-Xin Gao, Xiao-Lei Liu, Zheng-Hai Huang +4
Second-order optimization methods have the ability to accelerate convergence by modifying the gradient through the curvature matrix. There have been many attempts to use second-ord…