Z. Zhao, Q. Liu, K. Zhou, Z. Liu, Y. Shao, Z. Hu, B. Huang. Activation Control for Efficient Eliciting Long Chain-of-Thought Ability of Language Models. NeurIPS’25.
W. Wu, Z. Song, K. Zhou, Y. Shao, Z. Hu, B. Huang. Towards General Continuous Memory for Vision-Language Models. NeurIPS’25.
H. Liang, S. Shi, Y. Zhang, B. Huang, Y. Du.Causality Meets Locality: Provably Generalizable and Scalable Policy Learning for Network Systems. NeurIPS’25.
W. Wang, M. Gong, B. Huang, J. Bailey, B. Han, K. Zhang, F. Liu. Practical Kernel Selection for Kernel-Based Conditional Independence Test. NeurIPS’25.
X. Wang, B. Huang. Modeling Unseen Environments with Language-Guided Composable Causal Components in Reinforcement Learning. ICLR’25. [pdf]
P. Prashant, I. Ng, K. Zhang, B. Huang. Differential Causal Discovery for Latent Hierarchical Causal Models. ICLR’25. [pdf]
Y. Lin, Y. Huang, W. Liu, H. Deng, I. Ng, K, Zhang, M. Gong, Y. Ma, B. Huang. A Skewness-based Criterion for Addressing Heterogeneous Noise in Causal Discovery. ICLR’25. [pdf]
Y. Yang, B. Huang, F. Feng, X. Wang, S. Tu, L. Xu. Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations. ICLR’25. [pdf]
Z. Zhang, I. Ng, D. Gong, Y. Liu, M. Gong, B. Huang, K. Zhang, A. Hengel, J. Shi. Analytic DAG Constraints for Differential DAG Learning. ICLR’25. [pdf]
W. Liu, H. Hou, E. Gao, B. Huang, Q. Ke, H. Bondell, M. Gong. MissScore: High-Order Score Estimation in the Presence of Missing Data. ICML’25.
Y. Liu, Z. Zhang, D. Gong, M. Gong, B. Huang, A. Hengel, K, Zhang, J. Shi. Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation. TMLR’25.
[CD] X. Feng*, B. Huang*, Z. Chen, R. Cai, C. Glymour, Z. Geng, K. Zhang. Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables. JMLR’24. [pdf]
[CD] Y. Zheng, B. Huang, W. Chen, J. Ramsey, M. Gong, R. Cai, S. Shimizu, P. Spirtes, K. Zhang. Causal-learn: Causal Discovery in Python. JMLR’24. [pdf]
[CD][ML/AI] G. Hao, J. Zhang, B. Huang, J. Zhang, K. Zhang. Natural Counterfactuals with Necessary Backtracking. NeurIPS’24.
[ML/AI][CD]L. Kong, G. Chen, B. Huang, E. Xing, Y. Chi, K. Zhang. Latent Discrete Concepts in Latent Hierarchical Models. NeurIPS’24.
[CD] Y. Wang, B. Huang, W. Huang, X. Geng, M. Gong. Identifiability Analysis of Linear ODE Systems with Hidden Confounders. NeurIPS’24.
[CD] L. Li, H. Dai, H. Ghothani, B. Huang, J. Zhang, S. Harel, I. Bentwich, G. Chen, K. Zhang. On Causal Discovery in the Presence of Deterministic Relations. NeurIPS’24.
[CD] X. Dong, I. Ng, B. Huang, Y. Sun, S. Jin, R. Legaspi, P. Spirtes, K. Zhang. On the Parameter Identifiability of Partially Observed Linear Causal Models. NeurIPS’24.
[ML/AI][CD] Y. Sun, B. Huang, Y. Yao, D. Zeng, X. Dong, S. Jin, B. Sun, R. Legaspi, K. Ikeda, P. Spirtes, K. Zhang. Identifying Latent State-Transition Processes for Individualized Reinforcement Learning. NeurIPS’24.
[CD] I. Ng, X. Dong, H. Dai, B. Huang, P. Spirtes, K. Zhang. Score-based Causal Discovery of Latent Variable Causal Models. ICML’24. [pdf]
[CD] W. Wang, B. Huang, F. Liu, X. You, T. Liu, K. Zhang, M. Gong. Optimal Kernel Choice for Score Function-based Causal Discovery. ICML’24. [pdf]
[CD][ML/AI] Q. Huang, C. Meng, D. Cao, B. Huang, Y. Chang, Y. Liu. An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series. ICML’24. [pdf]
[CD] X. Dong*, B. Huang*, I. Ng, X. Song, Y. Zheng, S. Jin, R. Legaspi, P. Spirtes, K. Zhang. A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables. ICLR’24. [pdf]
[CD] S. Jin, F. Xie, G. Chen, B. Huang, Z. Chen, X. Dong, K. Zhang. Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability. ICLR’24. [pdf]
[ML/AI][CD] Y. Liu, Z. Zhang, D. Gong, M. Gong, B. Huang, A. Hengel, K. Zhang, J. Shi. Identifiable Latent Polynomial Causal Models through the Lens of Change. ICLR’24. [pdf]
[CD] L. Li, I. Ng, G. Luo, B. Huang, G. Chen, T. Liu, B. Gu, K. Zhang. Federated Causal Discovery from Heterogeneous Data. ICLR’24. [pdf]
[ML/AI]Y. Yang, B. Huang, S. Tu, L. Xu. Boosting Efficiency in Task-Agnostic Exploration Through Causal Knowledge. IJCAI’24. [pdf]
[ML/AI][CD]Y. Sun, E. Wang, B. Huang, C. Lu, L. Feng, C. Sun, K. Zhang. ACAMDA: Improving Data Efficiency in Reinforcement Learning Through Guided Counterfactual Data Augmentation. AAAI’24. [pdf]
[CD]I. Ng, B. Huang, K. Zhang. Structure Learning with Continuous Optimization: A Sober Look and Beyond. CLeaR’24 (Best Paper Award). [pdf]
[CD]W. Liu, B. Huang, E. Gao, Q. Ke, H. Bondell, M. Gong. Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach. CLeaR’24. [pdf]
G. Zhou, Z. Han, S. Chen, B. Huang, L. Zhu, T. Liu, L. Yao, K. Zhang. HVCP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization. IEEE Transactions on Multimedia’24. [pdf]
[CD]Y. Wang, X. Geng, W. Huang, B. Huang, M. Gong. Generator Identification for Linear SDEs with Additive and Multiplicative Noise. NeurIPS’23. [pdf]
[ML/AI]Y. Liu*, B. Huang*, Z. Zhu, H. Tian, M. Gong, Y. Yu, K. Zhang. Learning World Models with Identifiable Factorization. NeurIPS’23. [pdf]
[CD][ML/AI]L. Kong, B. Huang, F. Xie, E. Xing, Y. Chi, K. Zhang. Identification of Nonlinear Latent Hierarchical Models. NeurIPS’23. [pdf]
[ML/AI]Y. Zhang, Y. Du, B. Huang, Z. Wang, J. Wang, M. Fang, M. Pechenizkiy. Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach. NeurIPS’23. [pdf]
[CD]B. Huang*, C. Low*, X. Feng, C. Glymour, K. Zhang. Latent Hierarchical Causal Structure Discovery with Rank Constraints. NeurIPS’22. [pdf]
[CD] F. Xie, B. Huang, Z. Chen, Y. He, Z. Geng, K. Zhang. Identification of Linear Non-Gaussian Latent Hierarchical Structure. ICML’22. [pdf]
[ML/AI]F. Feng, B. Huang, K. Zhang, S. Magliacane. Factored Adaptation for Non-Stationary Reinforcement Learning. NeurIPS’22. [pdf]
[ML/AI]B.Huang*, C. Lu*, L. Liu, J. M. Hernandez-Lobato, C. Glymour, B. Scholkopf, K. Zhang. Action-Sufficient State Representation Learning for Control with Structural Constraints. ICML’22. [pdf]
[ML/AI]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]
[SD]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]
[ML/AI]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]
[CD]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]
[CD] 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]
[ML/AI] 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]
[ML/AI] 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]
[CD]B. Huang, K. Zhang, M. Gong, C. Glymour. Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets. AAAI’20. [pdf]
[CD][ML/AI]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]
[CD][ML/AI] B. Huang, K. Zhang, M. Gong, C. Glymour. Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. ICML’19.[pdf]
[SD] 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]
[CD]A. Ghassami, N. Kiyavash, B. Huang, K. Zhang. Multi-Domain Causal Structure Learning in Linear Systems. NeurIPS’18. [pdf]
[CD] B. Huang, K. Zhang, Y. Lin, B. Schölkopf, C. Glymour. Generalized Score Functions for Causal Discovery. KDD’18: 1551-1560 (long presentation). [pdf]
[CD]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]
[CD]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]
[CD]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]
[CD] 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.
Biwei Huang 