Publications

[Google Scholar Link]

Papers:

  • 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]
  • B. Huang, K. Zhang, R. Sanchez-Romero, J. Ramsey, M. Glymour, C. Glymour. Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data. arXiv preprint arXiv: 1902.10073, 2019. [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]

Posters: 

  • 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.
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