9 citations · 35 across the 13 of their papers we have counts for
14 papers
Adaptive Softassign via Hadamard-Equipped Sinkhorn
Binrui Shen, Qiang Niu, Shengxin Zhu
Softassign is a pivotal method in graph matching and other learning tasks. Many softassign-based algorithms exhibit performance sensitivity to a parameter in the softassign. Howeve…
Suboptimal subspace construction for log-determinant approximation
Zongyuan Han, Wenhao Li, Yixuan Huang +1
Variance reduction is a crucial idea for Monte Carlo simulation and the stochastic Lanczos quadrature method is a dedicated method to approximate the trace of a matrix function. In…
An analysis on stochastic Lanczos quadrature with asymmetric quadrature nodes
Wenhao Li, Yixuan Huang, Shengxin Zhu
This paper revisits the error analysis of the Stochastic Lanczos Quadrature (SLQ) method for approximating the trace of matrix functions, with a specific focus on asymmetric Lanczo…
Learning with linear mixed model for group recommendation systems
Baode Gao, Guangpeng Zhan, Hanzhang Wang +2
Accurate prediction of users' responses to items is one of the main aims of many computational advising applications. Examples include recommending movies, news articles, songs, jo…
CSGO: Constrained-Softassign Gradient Optimization For Large Graph Matching
Binrui Shen, Qiang Niu, Shengxin Zhu
Graph matching aims to find correspondences between two graphs. This paper integrates several well-known graph matching algorithms into a framework: the constrained gradient method…
Generalized Rough Polyharmonic Splines for Multiscale PDEs with Rough Coefficients
Xinliang Liu, Lei Zhang, Shengxin Zhu
In this paper, we demonstrate the construction of generalized Rough Polyhamronic Splines (GRPS) within the Bayesian framework, in particular, for multiscale PDEs with rough coeffic…
Fabricated Pictures Detection with Graph Matching
Binrui Shen, Qiang Niu, Shengxin Zhu
Fabricating experimental pictures in research work is a serious academic misconduct, which should better be detected in the reviewing process. However, due to large number of submi…
Sparse inversion for derivative of log determinant
Shengxin Zhu, Andrew J Wathen
Algorithms for Gaussian process, marginal likelihood methods or restricted maximum likelihood methods often require derivatives of log determinant terms. These log determinants are…
Summation of Gaussian shifts as Jacobi's third Theta function
Shengxin Zhu
A proper choice of parameters of the Jacobi modular identity (Jacobi Imaginary transformation) implies that the summation of Gaussian shifts on infinity periodic grids can be repre…
Essential formulae for restricted maximum likelihood and its derivatives associated with the linear mixed models
Shengxin Zhu, Andrew J Wathen
The restricted maximum likelihood method enhances popularity of maximum likelihood methods for variance component analysis on large scale unbalanced data. As the high throughput bi…
Computing log-likelihood and its derivatives for restricted maximum likelihood methods
Shengxin Zhu
Recent large scale genome wide association analysis involves large scale linear mixed models. Quantifying (co)-variance parameters in the mixed models with a restricted maximum lik…
Information Splitting for Big Data Analytics
Shengxin Zhu, Tongxiang Gu, Xiaowen Xu +1
Many statistical models require an estimation of unknown (co)-variance parameter(s) in a model. The estimation usually obtained by maximizing a log-likelihood which involves log de…
Computing Overlaps of Two Quadrilateral Mesh Of the Same Connectivity
Xihua Xu, Shengxin Zhu
An exact conservative remapping scheme requires overlaps between two meshes and a reconstruction scheme on the old cells (Lagrangian mesh). While the are intensive discussion on re…
AIMS:Average Information Matrix Splitting
Shengxin Zhu, Tongxiang Gu, Xingping Liu
For linear mixed models with co-variance matrices which are not linearly dependent on variance component parameters, we prove that the average of the observed information and the F…