Zobrazeno 1 - 10
of 3 113
pro vyhledávání: '"Qiao, Jie"'
Autor:
Chen, Zhengming, Cai, Ruichu, Xie, Feng, Qiao, Jie, Wu, Anpeng, Li, Zijian, Hao, Zhifeng, Zhang, Kun
Unobserved discrete data are ubiquitous in many scientific disciplines, and how to learn the causal structure of these latent variables is crucial for uncovering data patterns. Most studies focus on the linear latent variable model or impose strict c
Externí odkaz:
http://arxiv.org/abs/2406.07020
Causal effect estimation under networked interference is an important but challenging problem. Available parametric methods are limited in their model space, while previous semiparametric methods, e.g., leveraging neural networks to fit only one sing
Externí odkaz:
http://arxiv.org/abs/2405.03342
Count data naturally arise in many fields, such as finance, neuroscience, and epidemiology, and discovering causal structure among count data is a crucial task in various scientific and industrial scenarios. One of the most common characteristics of
Externí odkaz:
http://arxiv.org/abs/2403.16523
Publikováno v:
Journal of Medical Internet Research, Vol 22, Iss 9, p e19096 (2020)
BackgroundPreviously, we reported a model for assessing ovarian reserves using 4 predictors: anti-Müllerian hormone (AMH) level, antral follicle count (AFC), follicle-stimulating hormone (FSH) level, and female age. This model is referred as the AAF
Externí odkaz:
https://doaj.org/article/de9b1b57dd8744cb99fad428f8723772
Autor:
Xu, Huiyu, Feng, Guoshuang, Wei, Yuan, Feng, Ying, Yang, Rui, Wang, Liying, Zhang, Hongxia, Li, Rong, Qiao, Jie
Publikováno v:
JMIR Medical Informatics, Vol 8, Iss 4, p e17366 (2020)
BackgroundEctopic pregnancy (EP) is a serious complication of assisted reproductive technology (ART). However, there is no acknowledged mathematical model for predicting EP in the ART population. ObjectiveThe goal of the research was to establish a
Externí odkaz:
https://doaj.org/article/dbcac05f29794c56b9f7e52a540869db
Autor:
Cai, Ruichu, Huang, Siyang, Qiao, Jie, Chen, Wei, Zeng, Yan, Zhang, Keli, Sun, Fuchun, Yu, Yang, Hao, Zhifeng
As a key component to intuitive cognition and reasoning solutions in human intelligence, causal knowledge provides great potential for reinforcement learning (RL) agents' interpretability towards decision-making by helping reduce the searching space.
Externí odkaz:
http://arxiv.org/abs/2402.04869
Deep neural networks (DNNs) have been demonstrated to be vulnerable to well-crafted \emph{adversarial examples}, which are generated through either well-conceived $\mathcal{L}_p$-norm restricted or unrestricted attacks. Nevertheless, the majority of
Externí odkaz:
http://arxiv.org/abs/2312.13628
Missing data are an unavoidable complication frequently encountered in many causal discovery tasks. When a missing process depends on the missing values themselves (known as self-masking missingness), the recovery of the joint distribution becomes un
Externí odkaz:
http://arxiv.org/abs/2312.12206
Autor:
Liu, Yuequn, Cai, Ruichu, Chen, Wei, Qiao, Jie, Yan, Yuguang, Li, Zijian, Zhang, Keli, Hao, Zhifeng
Learning Granger causality from event sequences is a challenging but essential task across various applications. Most existing methods rely on the assumption that event sequences are independent and identically distributed (i.i.d.). However, this i.i
Externí odkaz:
http://arxiv.org/abs/2306.14114
Learning causal structure among event types from discrete-time event sequences is a particularly important but challenging task. Existing methods, such as the multivariate Hawkes processes based methods, mostly boil down to learning the so-called Gra
Externí odkaz:
http://arxiv.org/abs/2305.05986