1 citations · 3 across the 8 of their papers we have counts for
6 papers
Achieving Consensus over Compact Submanifolds
Jiang Hu, Jiaojiao Zhang, Kangkang Deng
We consider the consensus problem in a decentralized network, focusing on a compact submanifold that acts as a nonconvex constraint set. By leveraging the proximal smoothness of th…
KOO approach for scalable variable selection problem in large-dimensional regression
Zhidong Bai, Kwok Pui Choi, Yasunori Fujikoshi +1
An important issue in many multivariate regression problems is to eliminate candidate predictors with null predictor vectors. In large-dimensional (LD) setting where the numbers of…
Decentralized Riemannian natural gradient methods with Kronecker-product approximations
Jiang Hu, Kangkang Deng, Na Li +1
With a computationally efficient approximation of the second-order information, natural gradient methods have been successful in solving large-scale structured optimization problem…
A projected semismooth Newton method for a class of nonconvex composite programs with strong prox-regularity
Jiang Hu, Kangkang Deng, Jiayuan Wu +1
This paper aims to develop a Newton-type method to solve a class of nonconvex composite programs. In particular, the nonsmooth part is possibly nonconvex. To tackle the nonconvexit…
Sampling without replacement from a high-dimensional finite population
Jiang Hu, Shaochen Wang, Yangchun Zhang +1
It is well known that most of the existing theoretical results in statistics are based on the assumption that the sample is generated with replacement from an infinite population.…
Riemannian Natural Gradient Methods
Jiang Hu, Ruicheng Ao, Anthony Man-Cho So +2
This paper studies large-scale optimization problems on Riemannian manifolds whose objective function is a finite sum of negative log-probability losses. Such problems arise in var…