Zobrazeno 1 - 10
of 144
pro vyhledávání: '"Lin, Meixia"'
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
We study a variety of Wasserstein distributionally robust optimization (WDRO) problems where the distributions in the ambiguity set are chosen by constraining their Wasserstein discrepancies to the empirical distribution. Using the notion of weak Lip
Externí odkaz:
http://arxiv.org/abs/2402.03942
Graphical models have exhibited their performance in numerous tasks ranging from biological analysis to recommender systems. However, graphical models with hub nodes are computationally difficult to fit, particularly when the dimension of the data is
Externí odkaz:
http://arxiv.org/abs/2308.08852
In this paper, we propose an adaptive sieving (AS) strategy for solving general sparse machine learning models by effectively exploring the intrinsic sparsity of the solutions, wherein only a sequence of reduced problems with much smaller sizes need
Externí odkaz:
http://arxiv.org/abs/2306.17369
The exclusive lasso (also known as elitist lasso) regularizer has become popular recently due to its superior performance on intra-group feature selection. Its complex nature poses difficulties for the computation of high-dimensional machine learning
Externí odkaz:
http://arxiv.org/abs/2306.14196
This paper presents a novel Fourier spectral method that utilizes optimization techniques to ensure the positivity and conservation of moments in the space of trigonometric polynomials. We rigorously analyze the accuracy of the new method and prove t
Externí odkaz:
http://arxiv.org/abs/2304.11847
Publikováno v:
In Sustainable Cities and Society 15 November 2024 115
Autor:
Geng, Hongkai, Lin, Tao, Han, Ji, Zheng, Yicheng, Zhang, Junmao, Jia, Zixu, Chen, Yuan, Lin, Meixia, Yu, Long, Zhang, Yukui
Publikováno v:
In Journal of Environmental Management November 2024 370
Autor:
Jia, Zixu, Lin, Tao, Guo, Xiangzhong, Zheng, Yicheng, Geng, Hongkai, Zhang, Junmao, Chen, Yuan, Liu, Wenhui, Lin, Meixia
Publikováno v:
In Journal of Hydrology November 2024 644