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 | 236 | 2020 |
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 | 165 | 2017 |
Generalized Score Functions for Causal Discovery B Huang, K Zhang, Y Lin, B Schölkopf, C Glymour KDD'18, 2018 | 160 | 2018 |
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 | 100 | 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 | 100 | 2018 |
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 | 94 | 2021 |
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 | 83 | 2019 |
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 | 82 | 2019 |
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 | 74 | 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 | 68 | 2018 |
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 | 67 | 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 | 61 | 2020 |
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 | 56 | 2024 |
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 | 56 | 2022 |
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 | 49 | 2022 |
Identification of time-dependent causal model: A gaussian process treatment B Huang, K Zhang, B Schölkopf Twenty-Fourth international joint conference on artificial intelligence, 2015 | 47 | 2015 |
Behind distribution shift: Mining driving forces of changes and causal arrows B Huang, K Zhang, J Zhang, R Sanchez-Romero, C Glymour, B Schölkopf 2017 IEEE International Conference on Data Mining (ICDM), 913-918, 2017 | 40 | 2017 |
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 | 37 | 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 | 35 | 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 | 33 | 2020 |