| Causal discovery from heterogeneous/nonstationary data B Huang, K Zhang, J Zhang, J Ramsey, R Sanchez-Romero, C Glymour, ... Journal of Machine Learning Research 21 (89), 1-53, 2020 | 342 | 2020 |
| Generalized Score Functions for Causal Discovery B Huang, K Zhang, Y Lin, B Schölkopf, C Glymour KDD'18, 2018 | 215 | 2018 |
| Causal discovery from nonstationary/heterogeneous data: Skeleton estimation and orientation determination K Zhang, B Huang, J Zhang, C Glymour, B Schölkopf IJCAI: Proceedings of the Conference 2017, 1347, 2017 | 184 | 2017 |
| Deeptrader: a deep reinforcement learning approach for risk-return balanced portfolio management with market conditions embedding Z Wang, B Huang, S Tu, K Zhang, L Xu Proceedings of the AAAI conference on artificial intelligence 35 (1), 643-650, 2021 | 181 | 2021 |
| Causal-learn: Causal discovery in python Y Zheng, B Huang, W Chen, J Ramsey, M Gong, R Cai, S Shimizu, ... Journal of Machine Learning Research 25 (60), 1-8, 2024 | 165 | 2024 |
| Generalized independent noise condition for estimating latent variable causal graphs F Xie, R Cai, B Huang, C Glymour, Z Hao, K Zhang Advances in neural information processing systems 33, 14891-14902, 2020 | 141 | 2020 |
| Tetrad—a toolbox for causal discovery JD Ramsey, K Zhang, M Glymour, RS Romero, B Huang, I Ebert-Uphoff, ... 8th international workshop on climate informatics, 1-4, 2018 | 138 | 2018 |
| Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models B Huang, K Zhang, M Gong, C Glymour International Conference of Machine Learning, 2019, 2019 | 107 | 2019 |
| Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods R Sanchez-Romero, JD Ramsey, K Zhang, MRK Glymour, B Huang, ... Network Neuroscience 3 (2), 274-306, 2019 | 106 | 2019 |
| 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 | 87 | 2022 |
| Domain adaptation as a problem of inference on graphical models K Zhang, M Gong, P Stojanov, B Huang, Q Liu, C Glymour Advances in neural information processing systems 33, 4965-4976, 2020 | 87 | 2020 |
| Adarl: What, where, and how to adapt in transfer reinforcement learning B Huang, F Feng, C Lu, S Magliacane, K Zhang arXiv preprint arXiv:2107.02729, 2021 | 85 | 2021 |
| Sample-efficient reinforcement learning via counterfactual-based data augmentation C Lu, B Huang, K Wang, JM Hernández-Lobato, K Zhang, B Schölkopf arXiv preprint arXiv:2012.09092, 2020 | 83 | 2020 |
| Multi-domain causal structure learning in linear systems AE Ghassami, N Kiyavash, B Huang, K Zhang Advances in neural information processing systems 31, 2018 | 82 | 2018 |
| Latent hierarchical causal structure discovery with rank constraints B Huang, CJH Low, F Xie, C Glymour, K Zhang Advances in neural information processing systems 35, 5549-5561, 2022 | 80 | 2022 |
| Action-sufficient state representation learning for control with structural constraints B Huang, C Lu, L Leqi, JM Hernández-Lobato, C Glymour, B Schölkopf, ... International Conference on Machine Learning, 9260-9279, 2022 | 59 | 2022 |
| Identification of Time-Dependent Causal Model: A Gaussian Process Treatment. B Huang, K Zhang, B Schölkopf IJCAI, 3561-3568, 2015 | 57 | 2015 |
| Factored adaptation for non-stationary reinforcement learning F Feng, B Huang, K Zhang, S Magliacane Advances in Neural Information Processing Systems 35, 31957-31971, 2022 | 55 | 2022 |
| Causal discovery from multiple data sets with non-identical variable sets B Huang, K Zhang, M Gong, C Glymour Proceedings of the AAAI conference on artificial intelligence 34 (06), 10153 …, 2020 | 45 | 2020 |
| Structure learning with continuous optimization: A sober look and beyond I Ng, B Huang, K Zhang Causal Learning and Reasoning, 71-105, 2024 | 42 | 2024 |
Kun ZhangCarnegie Mellon University & Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)Verified email at cmu.edu
