Zobrazeno 1 - 10
of 802
pro vyhledávání: '"Chen, Jiahua"'
Autor:
Zhang, Qiong, Chen, Jiahua
This paper proposes two split-and-conquer (SC) learning estimators for finite mixture models that are tolerant to Byzantine failures. In SC learning, individual machines obtain local estimates, which are then transmitted to a central server for aggre
Externí odkaz:
http://arxiv.org/abs/2407.13980
Autor:
Zhang, Archer Gong, Chen, Jiahua
In many statistical and econometric applications, we gather individual samples from various interconnected populations that undeniably exhibit common latent structures. Utilizing a model that incorporates these latent structures for such data enhance
Externí odkaz:
http://arxiv.org/abs/2309.09103
Autor:
Liang, Haodi, Chen, Jiahua
This paper develops several interesting, significant, and interconnected approaches to nonparametric or semi-parametric statistical inferences. The overwhelmingly favoured maximum likelihood estimator (MLE) under parametric model is renowned for its
Externí odkaz:
http://arxiv.org/abs/2303.16410
Aspect-based sentiment analysis (ABSA) typically requires in-domain annotated data for supervised training/fine-tuning. It is a big challenge to scale ABSA to a large number of new domains. This paper aims to train a unified model that can perform ze
Externí odkaz:
http://arxiv.org/abs/2202.01924
Autor:
Ling, Lin1,2 (AUTHOR), Chen, Jiahua2 (AUTHOR), Zhan, Lei1,2 (AUTHOR), Fu, Juanjuan2 (AUTHOR), He, Runhua2 (AUTHOR), Wang, Wenyan2 (AUTHOR), Wei, Bing2 (AUTHOR) weibing1965@163.com, Ma, Xiaofeng2 (AUTHOR) mxfeng0423@163.com, Cao, Yunxia1 (AUTHOR) caoyunxia6@126.com
Publikováno v:
Scientific Reports. 7/4/2024, Vol. 14 Issue 1, p1-12. 12p.
Autor:
Chen, Jiahua, Chen, Xinyi, Zhang, Baoquan, He, Li, Li, Xinjian, Li, Yingzhan, Zhang, Zhen, Zhou, Ying, Jin, Wanhui, He, Xia, Liu, Hongchen
Publikováno v:
In International Journal of Biological Macromolecules June 2024 271 Part 1
Autor:
Zhang, Qiong, Chen, Jiahua
When a population exhibits heterogeneity, we often model it via a finite mixture: decompose it into several different but homogeneous subpopulations. Contemporary practice favors learning the mixtures by maximizing the likelihood for statistical effi
Externí odkaz:
http://arxiv.org/abs/2107.01323
Autor:
Zhang, Archer Gong, Chen, Jiahua
In many applications, we collect independent samples from interconnected populations. These population distributions share some latent structure, so it is advantageous to jointly analyze the samples. One effective way to connect the distributions is
Externí odkaz:
http://arxiv.org/abs/2103.03445
Autor:
He, Mingxing, Chen, Jiahua
The finite Gamma mixture model is often used to describe randomness in income data, insurance data, and data from other applications. The popular likelihood approach, however, does not work for this model because the likelihood function is unbounded,
Externí odkaz:
http://arxiv.org/abs/2011.04058
Autor:
Zhang, Mingyu, Chu, Lei, Chen, Jiahua, Qi, Fuxun, Li, Xiaoyan, Chen, Xinliang, Yu, Deng-Guang
Publikováno v:
In Composites Part B 15 January 2024 269