Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Zhang, Yangjing"'
Matrix regression plays an important role in modern data analysis due to its ability to handle complex relationships involving both matrix and vector variables. We propose a class of regularized regression models capable of predicting both matrix and
Externí odkaz:
http://arxiv.org/abs/2410.19264
Autor:
Lin, Meixia, Zhang, Yangjing
We consider the problem of jointly learning row-wise and column-wise dependencies of matrix-variate observations, which are modelled separately by two precision matrices. Due to the complicated structure of Kronecker-product precision matrices in the
Externí odkaz:
http://arxiv.org/abs/2403.02608
The Riemannian Augmented Lagrangian Method (RALM), a recently proposed algorithm for nonsmooth optimization problems on Riemannian manifolds, has consistently exhibited high efficiency as evidenced in prior studies \cite{ZBDZ21,ZBD22}. It often demon
Externí odkaz:
http://arxiv.org/abs/2308.06793
Publikováno v:
SIAM Journal on Optimization, 33 (2023), pp. 2988-3011
Strong variational sufficiency is a newly proposed property, which turns out to be of great use in the convergence analysis of multiplier methods. However, what this property implies for non-polyhedral problems remains a puzzle. In this paper, we pro
Externí odkaz:
http://arxiv.org/abs/2210.04448
Publikováno v:
Journal of Machine Learning Research, 25 (2024), pp. 1-46
In this paper we study the computation of the nonparametric maximum likelihood estimator (NPMLE) in multivariate mixture models. Our first approach discretizes this infinite dimensional convex optimization problem by fixing the support points of the
Externí odkaz:
http://arxiv.org/abs/2208.07514
Publikováno v:
中国工程科学, Vol 26, Iss 1, Pp 202-215 (2024)
进入大数据时代后，互联网应用和信息服务全面普及，大量的个人敏感生物信息被收集整理，导致隐私泄露风险增加；事件相机作为新型的生物启发式传感器，具有低延迟、高动态、无纹理
Externí odkaz:
https://doaj.org/article/108ecf31c3bc4488b28ea36ea7a3a983
Publikováno v:
Journal of Machine Learning Research, 23 (2022), pp 1-39
Square-root (loss) regularized models have recently become popular in linear regression due to their nice statistical properties. Moreover, some of these models can be interpreted as the distributionally robust optimization counterparts of the tradit
Externí odkaz:
http://arxiv.org/abs/2109.03632
We consider the problem of learning a graph under the Laplacian constraint with a non-convex penalty: minimax concave penalty (MCP). For solving the MCP penalized graphical model, we design an inexact proximal difference-of-convex algorithm (DCA) and
Externí odkaz:
http://arxiv.org/abs/2010.11559
Publikováno v:
SIAM Journal on Optimization, 30 (2020) , 2197-2220
Undirected graphical models have been especially popular for learning the conditional independence structure among a large number of variables where the observations are drawn independently and identically from the same distribution. However, many mo
Externí odkaz:
http://arxiv.org/abs/1906.04647
Publikováno v:
SIAM Journal on Mathematics of Data Science, 3(2021), pp. 524-543
Nowadays, analysing data from different classes or over a temporal grid has attracted a great deal of interest. As a result, various multiple graphical models for learning a collection of graphical models simultaneously have been derived by introduci
Externí odkaz:
http://arxiv.org/abs/1902.06952