| Active learning of causal networks with intervention experiments and optimal designs YB He, Z Geng Journal of Machine Learning Research 9 (Nov), 2523-2547, 2008 | 210 | 2008 |
| Counting and Exploring Sizes of Markov Equivalence Classes of Directed Acyclic Graphs Y He Journal of Machine Learning Research 16, 2589-2609, 2015 | 47 | 2015 |
| Identification of linear non-gaussian latent hierarchical structure F Xie, B Huang, Z Chen, Y He, Z Geng, K Zhang International Conference on Machine Learning, 24370-24387, 2022 | 41 | 2022 |
| Associating stock prices with web financial information time series based on support vector regression X Liang, RC Chen, Y He, Y Chen Neurocomputing 115, 142-149, 2013 | 33 | 2013 |
| Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs Y He, J Jia, B Yu | 33 | 2013 |
| Molecular analysis of early rice stamen development using organ-specific gene expression profiling XC Lu, HQ Gong, ML Huang, SL Bai, YB He, X Mao, Z Geng, SG Li, L Wei, ... Plant molecular biology 61, 845-861, 2006 | 31 | 2006 |
| On low-rank directed acyclic graphs and causal structure learning Z Fang, S Zhu, J Zhang, Y Liu, Z Chen, Y He IEEE Transactions on Neural Networks and Learning Systems, 2023 | 14 | 2023 |
| Performance of deep reinforcement learning for high frequency market making on actual tick data Z Xu, X Cheng, Y He Proceedings of the 21st International Conference on Autonomous Agents and …, 2022 | 12 | 2022 |
| Causal network learning from multiple interventions of unknown manipulated targets Y He, Z Geng arXiv preprint arXiv:1610.08611, 2016 | 11 | 2016 |
| A local method for identifying causal relations under Markov equivalence Z Fang, Y Liu, Z Geng, S Zhu, Y He Artificial Intelligence 305, 103669, 2022 | 9 | 2022 |
| Ida with background knowledge Z Fang, Y He Conference on Uncertainty in Artificial Intelligence, 270-279, 2020 | 9 | 2020 |
| Formulas for counting the sizes of Markov equivalence classes of directed acyclic graphs Y He, B Yu arXiv preprint arXiv:1610.07921, 2016 | 9 | 2016 |
| Fast pruning superfluous support vectors in SVMs X Liang, Y Ma, Y He, L Yu, RC Chen, T Liu, X Yang, TS Chen Pattern Recognition Letters 34 (10), 1203-1209, 2013 | 9 | 2013 |
| A Bayesian network model for prediction of low or failed fertilization in assisted reproductive technology based on a large clinical real-world data T Tian, F Kong, R Yang, X Long, L Chen, M Li, Q Li, Y Hao, Y He, Y Zhang, ... Reproductive Biology and Endocrinology 21 (1), 8, 2023 | 7 | 2023 |
| Collapsible IDA: Collapsing parental sets for locally estimating possible causal effects Y Liu, Z Fang, Y He, Z Geng Conference on Uncertainty in Artificial Intelligence, 290-299, 2020 | 7 | 2020 |
| Local causal network learning for finding pairs of total and direct effects Y Liu, Z Fang, Y He, Z Geng, C Liu Journal of Machine Learning Research 21 (148), 1-37, 2020 | 7 | 2020 |
| Learning causal structures based on Markov equivalence class YB He, Z Geng, X Liang Algorithmic Learning Theory: 16th International Conference, ALT 2005 …, 2005 | 7 | 2005 |
| Testability of instrumental variables in linear non-Gaussian acyclic causal models F Xie, Y He, Z Geng, Z Chen, R Hou, K Zhang Entropy 24 (4), 512, 2022 | 6 | 2022 |
| Relationship of causal effects in a causal chain and related inference Z Geng, Y He, X Wang Science in China Series A: Mathematics 47, 730-740, 2004 | 6 | 2004 |
| Bayesian method for learning graphical models with incompletely categorical data Z Geng, YB He, XL Wang, Q Zhao Computational statistics & data analysis 44 (1-2), 175-192, 2003 | 6 | 2003 |
