[Google Scholar Link]


  • B. Huang*, C. Low*, X. Feng, C. Glymour, K. Zhang. Latent Hierarchical Causal Structure Discovery with Rank Constraints. NeurIPS’22. [pdf]
  • F. Feng, B. Huang, K. Zhang, S. Magliacane. Factored Adaptation for Non-Stationary Reinforcement Learning. NeurIPS’22. [pdf]
  • F. Xie, B.Huang, Z. Chen, Y. He, Z. Geng, K. Zhang. Identification of Linear Non-Gaussian Latent Hierarchical Structure. ICML’22. [pdf]
  • B. Huang*, C. Lu*, L. Liu, J. M. Hernandez-Lobato, C. Glymour, B. Schölkopf, K. Zhang. Action-Sufficient State Representation Learning for Control with Structural Constraints. ICML’22. [pdf]
  • B. Huang, F. Feng, C. Lu, S. Magliacane, K. Zhang. AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning. ICLR’22 (spotlight). [pdf]
  • W. Chen, K. Zhang, R. Cai, B. Huang, J. Ramsey, Z. Hao, C. Glymour. FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders. arXiv preprint, arXiv:2103.14238, 2021. [pdf]
  • B. Huang. Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data. Neural Engineering Techniques for Autism Spectrum Disorder, 2021. [book website] [pdf]
  • Z. Wang, B. Huang, S. Tu, K. Zhang, L.Xu. DeepTrader: A Deep Reinforcement Learning Approach to Risk-Return Balanced Portfolio Management with Market Conditions Embedding. AAAI‘21. [pdf]
  • C. Lu*, B. Huang*, K. Wang, K. Zhang, J. M. Hernandez-Lobato, B. Schölkopf. Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation. Offline RL Workshop, NeurIPS’20. [pdf]
  • B. Huang*, K. Zhang*, J. Zhang, J. Ramsey, R. Sanchez-Romero, C. Glymour, B. Schölkopf. Causal Discovery from Heterogeneous/Nonstationary Data. JMLR, 21(89), 2020. [pdf]
  • K. Zhang*, M. Gong*, P. Stojanov, B. Huang, Q. Liu, and C. Glymour. Domain Adaptation As a Problem of Inference on Graphical Models. NeurIPS’20. [pdf]
  • F. Xie, R. Cai, B. Huang, C. Glymour, Z. Hao, K. Zhang, Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs. NeurIPS’20 (spotlight). [pdf]
  • B. Huang, K. Zhang, M. Gong, C. Glymour. Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets. AAAI’20. [pdf]
  • B. Huang, K. Zhang, P. Xie, M. Gong, E. Xing, C. Glymour. Specific and Shared Causal Relation Modeling and Mechanism-based Clustering. NeurIPS’19.[pdf]
  • B. Huang, K. Zhang, M. Gong, C. Glymour. Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. ICML’19.[pdf]
  • A. Ghassami, N. Kiyavash, B. Huang, K. Zhang. Multi-Domain Causal Structure Learning in Linear Systems. NeurIPS’18. [pdf]
  • B. Huang, K. Zhang, Y. Lin, B. Schölkopf, C. Glymour. Generalized Score Functions for Causal Discovery. KDD’18: 1551-1560 (long presentation). [pdf]
  • R. Sanchez-Romero, J. D. Ramsey, K. Zhang, M. R. K. Glymour, B. Huang, C. Glymour. Causal Discovery of Feedback Networks with Functional Magnetic Resonance Imaging. Network Neuroscience: 1-51. [pdf]
  • B. Huang, K. Zhang, J. Zhang, R. Sanchez-Romero, C. Glymour, B. Schölkopf. Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrow. ICDM’17: 913-918. [pdf]
  • K. Zhang, B. Huang, J. Zhang, C. Glymour, B. Schölkopf. Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination. IJCAI’17: 1347-1353 (long presentation). [pdf]
  • K. Zhang, J. Zhang, B. Huang, B. Schölkopf, C. Glymour. On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection. UAI’16: 825-834 (plenary talk session). [pdf]
  • B. Huang, K. Zhang, B. Schölkopf. Identification of Time-Dependent Causal Model: A Gaussian Process Treatment. IJCAI’15: 3561-3568. [pdf]


  • B. Huang, Y. Wen, Z. Yang. Identification of Causal Genetic Network for Alzheimer’s Disease. Artificial Intelligence for Data Recovery and Reuse, Pittsburgh, 2019 (Best Student Poster Award).
  • B. Huang, K. Zhang, R. S. Romero, J.D. Ramsey, M. Glymour, C. Glymour. Diagnosis of autism spectrum disorder by causal connectivity strength from resting state functional magnetic resonance imaging data. Society for Neuroscience, Washington, 2017.
  • Y. Lin, B. Huang, K. Zhang, and C. J. McManus. Causal inference in gene expression regulation from large-scale datasets. Cold Spring Harbor Meeting: Eukaryotic mRNA Processing, 2017.
  • R. S. Romero, J.D. Ramsey, K. Zhang, M. Glymour, B. Huang, C. Glymour. Discovery of fMRI Networks with Feedback Structures. International Workshop on Pattern Recognition in Neuroimaging, Toronto, 2017.
  • J.D. Ramsey, M. Glymour, R. S. Romero, B. Huang, K. Zhang, C. Glymour. Discovering high-dimensional directed networks of the human brain using the FGES algorithm for up to a million variables. Complex Systems Conference, Cancun, 2017.
  • M. Glymour, R. S. Romero, J. D. Ramsey, K. Zhang, B. Huang, C. Glymour. Fusiform and Cerebellum rs-fMRI Connectivity Implicated in ASD. Neuroscience Society Meeting, New York, 2016.