Zobrazeno 1 - 10
of 2 791
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
Autor:
He, Liping, Chen, Yong, Liang, Ziqiao, Li, Youzhi, Zhou, Minglin, Yuan, Zhiquan, Luo, Ling, Jin, Zhen, Yang, Yunyun, Chen, Jianxin
Publikováno v:
In Journal of Pharmaceutical and Biomedical Analysis 30 May 2016 124:129-137
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
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
The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications, yet it remains a significant challenge. A convincing explanation should be both necessary and sufficient simultaneously. However, existing GNN explaining appr
Externí odkaz:
http://arxiv.org/abs/2212.07056