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2020 – today
- 2024
[j7]Ruiqi Zhao
, Xiaoxia Yang, Yangbo He:
Learning Causes of Functional Dynamic Targets: Screening and Local Methods. Entropy 26(7): 541 (2024)- [j6]Zhuangyan Fang, Shengyu Zhu, Jiji Zhang, Yue Liu, Zhitang Chen, Yangbo He:
On Low-Rank Directed Acyclic Graphs and Causal Structure Learning. IEEE Trans. Neural Networks Learn. Syst. 35(4): 4924-4937 (2024) - [i6]Qingyuan Zheng, Yue Liu, Yangbo He:
Local Causal Discovery with Background Knowledge. CoRR abs/2408.07890 (2024) - 2023
- [j5]Feng Xie, Yan Zeng, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang:
Causal discovery of 1-factor measurement models in linear latent variable models with arbitrary noise distributions. Neurocomputing 526: 48-61 (2023) - [c5]Ruiqi Zhao, Lei Zhang, Shengyu Zhu, Zitong Lu, Zhenhua Dong, Chaoliang Zhang, Jun Xu, Zhi Geng, Yangbo He:
Conditional counterfactual causal effect for individual attribution. UAI 2023: 2519-2528 - 2022
- [j4]Zhuangyan Fang, Yue Liu, Zhi Geng, Shengyu Zhu, Yangbo He:
A local method for identifying causal relations under Markov equivalence. Artif. Intell. 305: 103669 (2022) - [j3]Feng Xie, Yangbo He, Zhi Geng, Zhengming Chen, Ru Hou, Kun Zhang:
Testability of Instrumental Variables in Linear Non-Gaussian Acyclic Causal Models. Entropy 24(4): 512 (2022) - [c4]Ziyi Xu, Xue Cheng, Yangbo He:
Performance of Deep Reinforcement Learning for High Frequency Market Making on Actual Tick Data. AAMAS 2022: 1765-1767 - [c3]Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang:
Identification of Linear Non-Gaussian Latent Hierarchical Structure. ICML 2022: 24370-24387 - [i5]Zhuangyan Fang, Ruiqi Zhao, Yue Liu, Yangbo He:
On the Representation of Causal Background Knowledge and its Applications in Causal Inference. CoRR abs/2207.05067 (2022) - 2021
- [i4]Zhuangyan Fang, Yue Liu, Zhi Geng, Yangbo He:
A Local Method for Identifying Causal Relations under Markov Equivalence. CoRR abs/2102.12685 (2021) - 2020
- [j2]Yue Liu, Zhuangyan Fang, Yangbo He, Zhi Geng, Chunchen Liu:
Local Causal Network Learning for Finding Pairs of Total and Direct Effects. J. Mach. Learn. Res. 21: 148:1-148:37 (2020) - [c2]Zhuangyan Fang, Yangbo He:
IDA with Background Knowledge. UAI 2020: 270-279 - [c1]Yue Liu, Zhuangyan Fang, Yangbo He, Zhi Geng:
Collapsible IDA: Collapsing Parental Sets for Locally Estimating Possible Causal Effects. UAI 2020: 290-299 - [i3]Zhuangyan Fang, Shengyu Zhu, Jiji Zhang, Yue Liu, Zhitang Chen, Yangbo He:
Low Rank Directed Acyclic Graphs and Causal Structure Learning. CoRR abs/2006.05691 (2020)
2010 – 2019
- 2016
- [i2]Yangbo He, Bin Yu:
Formulas for Counting the Sizes of Markov Equivalence Classes of Directed Acyclic Graphs. CoRR abs/1610.07921 (2016) - 2015
- [j1]Yangbo He, Jinzhu Jia, Bin Yu:
Counting and exploring sizes of Markov equivalence classes of directed acyclic graphs. J. Mach. Learn. Res. 16: 2589-2609 (2015) - 2012
- [i1]Yangbo He, Jinzhu Jia, Bin Yu:
Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs. CoRR abs/1209.5860 (2012)
Coauthor Index
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