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On Low-Rank Directed Acyclic Graphs and Causal Structure Learning

  • Published:2023 Issue: Volume: Page:1-14
  • ISSN:2162-237X
  • Container-title:IEEE Transactions on Neural Networks and Learning Systems
  • language:
  • Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Fang Zhuangyan1ORCID,Zhu Shengyu2ORCID,Zhang Jiji3ORCID,Liu Yue4ORCID,Chen Zhitang2,He Yangbo5

Affiliation:

1. School of Mathematical Sciences, Peking University, Beijing, China

2. Noah’s Ark Laboratory, Huawei Technologies, Hong Kong, Hong Kong

3. Department of Philosophy, The Chinese University of Hong Kong, Hong Kong, Hong Kong

4. Center for Applied Statistics and the School of Statistics, Renmin University of China, Beijing, China

5. LMAM, LMEQF, and the Center of Statistical Science, School of Mathematical Sciences, Peking University, Beijing, China

Funder

Research Grants Council of Hong Kong

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Identifiability and Consistent Estimation for Gaussian Chain Graph Models;Journal of the American Statistical Association;2024-01-12

2. Causal Graph Discovery From Self and Mutually Exciting Time Series;IEEE Journal on Selected Areas in Information Theory;2023
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