Yujia, Kun Zhang, “Generalizing Nonlinear ICA Beyond Structural Sparsity,” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2023 (oral)
Yu-Ren Liu, Biwei Huang, Zhengmao Zhu, Honglong Tian, Mingming Gong, Yang Yu, Kun Zhang, “Learning World Models with Identifiable Factorization,” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2023
Xiangchen Song, Weiran Yao, Yewen Fan, Xinshuai Dong, Guangyi Chen, Juan Carlos Niebles, Eric Xing, Kun Zhang, “Temporally Disentangled Representation Learning under Unknown Nonstationarity,” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2023
Hanqi Yan, Lingjing Kong, Lin Gui, Yuejie Chi, Eric Xing, Yulan He, Kun Zhang, “Counterfactual Generation with Identifiability Guarantee,” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2023
Lingjing Kong, Biwei Huang, Feng Xie, Eric Xing, Yuejie Chi, Kun Zhang, “Identification of Nonlinear Latent Hierarchical Models,” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2023
Ignavier Ng, Yujia Zheng, Xinshuai Dong, Kun Zhang, “On the Identifiability of Sparse ICA without Assuming Non-Gaussianity,” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2023
Zijian Li, Ruichu Cai, Guangyi Chen, Boyang Sun, Zhifeng Hao, Kun Zhang, “Subspace Identification for Multi-Source Domain Adaptation,” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2023 (spotlight)
Yang Liu, Hao Cheng, Kun Zhang, “Identifiability of Label Noise Transition Matrix,” accepted to International Conference on Machine Learning (ICML) 2023
Ruichu Cai, Zhiyi Huang, Wei Chen, Zhifeng Hao, Kun Zhang, “Causal Discovery with Latent Confounders Based on Higher-Order Cumulants,” accepted to International Conference on Machine Learning (ICML) 2023
Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song, Xinge You, Tongliang Liu, Kun Zhang, “Evolving Semantic Prototype Improves Generative Zero-Shot Learning,” accepted to International Conference on Machine Learning (ICML) 2023
Yu Yao, Mingming Gong, Yuxuan Du, Jun Yu, Bo Han, Kun Zhang, Tongliang Liu, “Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?” accepted to International Conference on Machine Learning (ICML) 2023
Jiaqi Sun, Lin Zhang, Guangyi Chen, Peng XU, Kun Zhang, Yujiu Yang, “Feature Expansion for Graph Neural Networks,” accepted to International Conference on Machine Learning (ICML) 2023
Yatong Chen, Zeyu Tang, Kun Zhang, Yang Liu, “Model Transferability with Responsive Decision Subjects,” accepted to International Conference on Machine Learning (ICML) 2023
Zeyu Tang, Jiji Zhang, Kun Zhang, “What-Is and How-To for Fairness in Machine Learning: A Survey, Reflection, and Perspective,” in ACM Computing Surveys 2023
Guangyi Chen, Zhenhao Chen, Shunxing Fan, Kun Zhang, “Munsupervised Sampling Promoting for Stochastic Human Trajectory Prediction,” in Proceedings of The Conference on Computer Vision and Pattern Recognition (CVPR) 2023
Lingjing Kong, Martin Q. Ma, Guangyi Chen, Eric Xing, Yuejie Chi, Louis-Philippe Morency, Kun Zhang, “Understanding Masked Autoencoders via Hierarchical Latent Variable Models,” in Proceedings of The Conference on Computer Vision and Pattern Recognition (CVPR) 2023
Shaoan Xie, Yanwu Xu, Mingming Gong, Kun Zhang, “Unpaired Image-to-Image Translation with Shortest Path Regularization,” in Proceedings of The Conference on Computer Vision and Pattern Recognition (CVPR) 2023
Shaoan Xie, Zhifei Zhang, Zhe Lin, Tobias Hinz, Kun Zhang, “SmartBrush: Diffusion-based Multi-modal Image Inpainting,” in Proceedings of The Conference on Computer Vision and Pattern Recognition (CVPR) 2023
Shaoan Xie, Lingjing Kong, Mingming Gong, Kun Zhang, “Multi-domain image generation and translation with identifiability guarantees,” accepted to International Conference on Learning Representations (ICLR) 2023
Yujia Zheng, Ignavier Ng, Yewen Fan, Kun Zhang, “Scalable Estimation of Nonparametric Markov Networks with Mixed-Type Data,” accepted to International Conference on Learning Representations (ICLR) 2023
Zeyu Tang, Yatong Chen, Yang Liu, Kun Zhang, “Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors,” accepted to International Conference on Learning Representations (ICLR) 2023
Yewen Fan, Nian Si, Kun Zhang, “Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems,” accepted to International Conference on Learning Representations (ICLR) 2023
Guangyi Chen, Weiran Yao, Xiangchen Song, Xinyue Li, Yongming Rao, Kun Zhang, “Prompt Learning with Optimal Transport for Vision-Language Models,” accepted to International Conference on Learning Representations (ICLR) 2023
Xinyi Wang, Michael Saxon, Jiachen Li, Hongyang Zhang, Kun Zhang, William Yang Wang, “Causal Balancing for Domain Generalization,” accepted to International Conference on Learning Representations (ICLR) 2023
Junlong Li, Guangyi Chen, Yansong Tang, Jinan Bao, Kun Zhang, Jie Zhou, Jiwen Lu, “GAIN: On the Generalization of Instructional Action Understanding,” accepted to International Conference on Learning Representations (ICLR) 2023
Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello, Scalable Causal Discovery with Score Matching,” accepted to the 2nd Conference on Causal Learning and Reasoning (CLeaR) 2023
Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello, “Causal Discovery with Score Matching on Additive Models with Arbitrary Noise,” accepted to the 2nd Conference on Causal Learning and Reasoning (CLeaR) 2023
Yue Yu, Xuan Kan, Hejie Cui, Ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang， "Deep DAG Learning of Effective Brain Connectivity for fMRI Analysis," accepted to IEEE International Symposium on Biomedical Imaging (ISBI) 2023
YHanin Al Ghothani and Kun Zhang， "Knee Injury Diagnosis with Data and Feature Fusion-Enhanced Multi-label Classification Network," accepted to IEEE International Symposium on Biomedical Imaging (ISBI) 2023
Yujia Zheng, Ignavier Ng, Kun Zhang, “On the Identifiability of Nonlinear ICA: Sparsity and Beyond,” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2022
Haoyue Dai, Peter Spirtes, Kun Zhang, “Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models,” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2022
Weiran Yao, Guangyi Chen, Kun Zhang, “Causal Disentanglement for Time Series,” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2022
Shaoan Xie, Qirong Ho, Kun Zhang, “Unsupervised Image-to-Image Translation with Density Changing Regularization,” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2022
Biwei Huang*, Charles Low*, Feng Xie, Clark Glymour, Kun Zhang, “Latent Hierarchical Causal Structure Discovery with Rank Constraints,” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2022
Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane, “Factored Adaptation for Non-Stationary Reinforcement Learning,” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2022
Yuqin Yang, AmirEmad Ghassami, Mohamed S Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser, “Causal Discovery in Linear Latent Variable Models Subject to Measurement Error,” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2022
Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong, “Counterfactual Fairness with Partially Known Causal Graph,” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2022
Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard Bondell, “MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models,” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2022
Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Ehsan M Abbasnejad, Mingming Gong, Kun Zhang, Javen Qinfeng Shi, “Truncated Matrix Power Iteration for Differentiable DAG Learning,” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2022
Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang, "Estimation of Linear Non-Gaussian Latent Hierarchical Structure," accepted to International Conference on Machine Learning (ICML) 2022
Lingjing Kong, Shaoan Xie, Weiran Yao, Yujia Zheng, Guangyi Chen, Petar Stojanov, Victor Akinwande, Kun Zhang, "Partial disentanglement for domain adaptation," accepted to International Conference on Machine Learning (ICML) 2022
Biwei, Chaochao Lu, Liu Leqi, Jose Miguel Hernandez-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang, "Action-Sufficient State Representation Learning for Control with Structural Constraints," accepted to International Conference on Machine Learning (ICML) 2022
Yanwu Xu, Shaoan Xie, Wenhao Wu, Kun Zhang, Mingming Gong, Kayhan Batmanghelic, "Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation," accepted to Conference on Computer Vision and Pattern Recognition (CVPR) 2022
Jiaxian Guo, Jiachen Li, Huan Fu, Mingming Gong, Kun Zhang, Dacheng Tao, "Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint," accepted to Conference on Computer Vision and Pattern Recognition (CVPR) 2022
Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang, "AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning," accepted to International Conference on Learning Representations (ICLR) 2022 (spotlight)
Weiran Yao, Yuewen Sun, Alex Ho, Changyin Sun, Kun Zhang, "Learning Temporally Latent Causal Processes from General Temporal Data," accepted to International Conference on Learning Representations (ICLR) 2022
Ruibo Tu, Kun Zhang, Hedvig Kjellstrom, Cheng Zhang, "Optimal Transport for Causal Discovery," accepted to International Conference on Learning Representations (ICLR) 2022 (spotlight)
Yao-Hung Hubert Tsai, Tianqin Li, Martin Q. Ma, Han Zhao, Kun Zhang, Louis-Philippe Morency, Ruslan Salakhutdinov, "Conditional Contrastive Learning with Kernel," accepted to International Conference on Learning Representations (ICLR) 2022
Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang, "Adversarial Robustness Through the Lens of Causality," accepted to International Conference on Learning Representations (ICLR) 2022
Zeyu Tang, Kun Zhang, "Attainability and Optimality: The Equalized Odds Fairness Revisited," accepted to the first Conference on Causal Learning and Reasoning (CLeaR), 2022
Ignavier Ng, Sebastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien, Kun Zhang, "On the Convergence of Continuous Constrained Optimization for Bayesian Network Structure Learning," accepted to International Conference on Artificial Intelligence and Statistics (AIStats) 2022
Ignavier Ng, Kun Zhang, "Towards Federated Bayesian Network Structure Learning with Continuous Optimization," accepted to International Conference on Artificial Intelligence and Statistics (AIStats) 2022
Sizhe Chen, Fan He, Xiaolin Huang, Kun Zhang, "Relevance Attack on Detectors," Pattern Recognition, 2022
Zhengmao Zhu, Shengyi Jiang, Yu-Ren Liu, Yang Yu, Kun Zhang, "Invariant Action Effect Model for Reinforcement Learning," accepted to the 26th AAAI conference on Artificial Intelligence (AAAI 2022)
Hao Zhang, Shuigeng Zhou, Kun Zhang, Jihong Guan, "Residual Similarity Based Conditional Independence Test and Its Application in Causal Discovery," accepted to the 26th AAAI conference on Artificial Intelligence (AAAI 2022)
Zhengming Chen, Feng Xie, Jie Qiao, Zhifeng Hao, Kun Zhang, Ruichu Cai, "Identification of Linear Latent Variable Model with Arbitrary Distribution," accepted to the 26th AAAI conference on Artificial Intelligence (AAAI 2022)
Petar Stojanov, Zijian Li, Mingming Gong, Ruichu Cai, Jaime G. Carbonell, Kun Zhang, “Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2021
Jeffrey Adams, Niels Richard Hansen, Kun Zhang, “Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases,” accepted to Conference on Neural Information Processing Systems (NeurIPS) 2021
Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang, "Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions," accepted to Conference on Neural Information Processing Systems (NeurIPS) 2021
Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang, "Instance-dependent Label-noise Learning under a Structural Causal Model," accepted to Conference on Neural Information Processing Systems (NeurIPS) 2021
Shaoan Xie, Mingming Gong, Yanwu Xu, and Kun Zhang, “Unaligned Image-to-Image Translation by Learning to Reweight,” In Proceedings of International Conference on Computer Vision (ICCV) 2021
Haiqin Yang, Xiaoyuan Yao, Yiqun Duan, Jianping Shen, Jie Zhong, Kun Zhang, "Progressive Open-Domain Response Generation with Multiple Controllable Attributes," In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI) 2021
Yuewen Sun, Kun Zhang, Changyin Sun, “Model-Based Transfer Reinforcement Learning Based on Graphical Model Representations,” IEEE Transactions on Neural Networks and Learning Systems, forthcoming, 2021
Jie Qiao, Ruichu Cai, Kun Zhang, Zhenjie Zhang, Zhifeng Hao, “Causal Discovery with Confounding Cascade Nonlinear Additive Noise Models," ACM Transactions on Intelligent Systems and Technology, forthcoming, 2021
Kun Zhang, “Computational Causal Discovery: Advantages and Assumptions,” Theoria, forthcoming, 2021 (Commentary on James Woodward's paper "Flagpoles anyone?: Causal and explanatory asymmetries")
M. R. Heydari, S. Salehkaleybar, Kun Zhang, “Adversarial Orthogonal Regression: Two Non-linear Regressions for Causal Inference,” Neural Networks, 143:66-73, 2021
Ni Lu, Kun Zhang, Changhe Yuan, “Improving Causal Discovery By Optimal Bayesian Network Learning,” In Proceedings of the 25th AAAI conference on Artificial Intelligence (AAAI 2021)
Zhicheng Wang, Biwei Huang, Shikui Tu, Kun Zhang, Lei Xu, “DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding,” In Proceedings of the 25th AAAI conference on Artificial Intelligence (AAAI 2021)
Hao Zhang, Kun Zhang, Shuigeng Zhou, Jihong Guan, Ji Zhang, “Testing Independence Between Linear Combinations for Causal Discovery,” In Proceedings of the 25th AAAI conference on Artificial Intelligence (AAAI 2021)
Wei Chen, Ruichu Cai, Kun Zhang, Zhifeng Hao, “Causal Discovery in Linear Non-Gaussian Acyclic Model with Multiple Latent Confounders,” IEEE Transactions on Neural Networks and Learning Systems, forthcoming, 2021
K. Zhang*, M. Gong*, P. Stojanov, B. Huang, Qingsong Liu, and C. Glymour, "Domain Adaptation As a Problem of Inference on Graphical Models," accepted to Conference on Neural Information Processing Systems (NeurIPS) 2020.
Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang, "Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Causal Graphs," accepted to Conference on Neural Information Processing Systems (NeurIPS) 2020 (spotlight).
Ignavier Ng, AmirEmad Ghassami, Kun Zhang, "On the Role of Sparsity and DAG Constraints for Learning Linear DAGs," accepted to Conference on Neural Information Processing Systems (NeurIPS) 2020.
Xueru Zhang*, Ruibo Tu*, Yang Liu, Mingyan Liu, Hedvig Kjellström, Kun Zhang, Cheng Zhang, "How do fair decisions fare in long-term qualification?" accepted to Conference on Neural Information Processing Systems (NeurIPS) 2020.
Cheng Zhang, Kun Zhang, Yingzhen Li, "A Causal View on Robustness of Neural Networks," accepted to Conference on Neural Information Processing Systems (NeurIPS) 2020.
Mingming Gong, Peng Liu, Frank C. Sciurba, Petar Stojanov, Dacheng Tao, George C. Tseng, Kun Zhang, Kayhan Batmanghelich, "Unpaired Data Empowers Association Tests", Bioinformatics, forthcoming, 2020.
Biwei Huang*, Kun Zhang*, Jiji Zhang, Joseph Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf, "Causal Discovery from Heterogeneous/Nonstationary Data," Journal of Machine Learning Research (JMLR), 2020
Chenghao Liu, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven C.H. Hoi, "Adaptive Task Sampling for Meta-Learning," In Proceedings of European Conference on Computer Vision (ECCV) 2020
AmirEmad Ghassami, Alan Yang, Negar Kiyavash, Kun Zhang, “Characterizing Distribution Equivalence for Cyclic and Acyclic Directed Graphs,” In Proceedings of International conference on Machine Learning (ICML) 2020, 2020
Jiaxian Guo, Mingming Gong, Tongliang Liu, Kun Zhang, Dacheng Tao, “LTF: A Label Transformation Framework for Correcting Target Shift,” In Proceedings of International conference on Machine Learning (ICML) 2020, 2020
Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, Dacheng Tao, “Label-Noise Robust Domain Adaptation,” In Proceedings of International conference on Machine Learning (ICML) 2020, 2020
Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash, Kun Zhang, “Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables,” Journal of Machine Learning Research (JMLR), 2020
Yige Zhang, Aaron Yi Ding, Jorg Ott, Mingxuan Yuan, Jia Zeng, Kun Zhang, Weixiong Rao , “Transfer Learning-Based Outdoor Position Recovery with Telco Data,” IEEE Transactions on Mobile Computing, 2020, forthcoming
Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour, “Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets,” In Proceedings of the 24th AAAI conference on Artificial Intelligence (AAAI 2020), New York, USA, 2020
Yanwu Xu, Mingming Gong, Junxiang Chen, Tongliang Liu, Kun Zhang, Kayhan Batmanghelich, “Generative-Discriminative Complementary Learning,” In Proceedings of the 24th AAAI conference on Artificial Intelligence (AAAI 2020), New York, USA, 2020
Ziye Chen, Yanwu Xu, Mingming Gong, Chaohui Wang, Bo Du, Kun Zhang, “Compressed Self-Attention for Deep Metric Learning,” In Proceedings of the 24th AAAI conference on Artificial Intelligence (AAAI 2020), New York, USA, 2020
Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Eric Xing, Clark Glymour, “Specific and Shared Causal Relation Modeling and Mechanism-based Clustering,“ accepted to Neural Information Processing Systems (NeurIPS) 2019, Vancouver, Canada, December 2019
Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang, “Triad Constraints for Learning Causal Structure of Latent Variables,“ accepted to Neural Information Processing Systems (NeurIPS) 2019, Vancouver, Canada, December 2019
Mingming Gong, Yanwu Xu, Chunyuan Li, Kun Zhang, Kayhan Batmanghelich, “Twin Auxilary Classifiers GAN,“ accepted to Neural Information Processing Systems (NeurIPS) 2019, Vancouver, Canada, December 2019
Chenwei Ding, Mingming Gong, Kun Zhang, Dacheng Tao, “Likelihood-Free Overcomplete ICA and ApplicationsIn Causal Discovery,“ accepted to Neural Information Processing Systems (NeurIPS) 2019, Vancouver, Canada, December 2019
Ruibo Tu, Kun Zhang, Bo Bertilson, Hedvig Kjellstrom, Cheng Zhang, “Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation,“ accepted to Neural Information Processing Systems (NeurIPS) 2019, Vancouver, Canada, December 2019
Yige Zhang, Weixiong Rao, Kun Zhang, Mingxuan Yuan, Jia Zeng, “PRNet: Outdoor Position Recovery for Heterogenous TelcoData by Deep Neural Network," accepted to the 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), Beijing, China, November 2019
Clark Glymour, Kun Zhang, Peter Spirtes, “A Review of Causal Discovery Algorithms Based on Graphical Models,” Frontiers in Genetics, 2019 (link)
Jakob Runge, Sebastian Bathiany, Erik Bollt, Gustau Camps-Valls, Dim Coumou, Ethan Deyle, Clark Glymour, Marlene Kretschmer, Miguel D. Mahecha, Jordi Muñoz-Marí, Egbert H. van Nes, Jonas Peters, Rick Quax, Markus Reichstein, Marten Scheffer, Bernhard Schölkopf, Peter Spirtes, George Sugihara, Jie Sun, Kun Zhang, Jakob Zscheischler, “Inferring Causation from Time Series in Earth System Sciences and Beyond,” Nature Communications, 2019 (link)
Ruben Sanchez-Romero, Joseph D. Ramsey, Kun Zhang, Madelyn R. K. Glymour, Biwei Huang and Clark Glymour, “Estimating Feedforward and Feedback Effective Connections from FMRI Time Series: Assessments of Statistical Methods,” Network Neuroscience, 3(2): 274-306, 2019 (link)
Joseph Ramsey, Kun Zhang, Clark Glymour, “The Evaluation of Discovery: Models, Simulation and Search through ‘Big Data’,” Open Philosophy, 2 (1):39-48 2019 (link)
Eric Strobl, Kun Zhang, and S. Visweswaran, “Approximate kernel-based conditional independence tests for fast non-parametric causal discovery,” Journal of Causal Inference, 7 (1), 2019 (link)
Ricardo Pio Monti, Kun Zhang, Aapo Hyvärinen, “Causal Discovery with General Non-Linear Relationships Using Non-Linear Independent Component Analysis,” in Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI) 2019 (link)
Shoubo Hu, Kun Zhang, Zhitang Chen, Laiwan Chan, “Domain Generalization via Multi-domain Discriminant Analysis,” in Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI) 2019
Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour, “Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models,” in Proceedings of International Conference on Machine Learning (ICML) 2019 (link)
Han Zhao, Remi Tachet des Combes, Kun Zhang, Geoffrey J. Gordon, “On Learning Invariant Representation for Domain Adaptation,” in Proceedings of International Conference on Machine Learning (ICML) 2019 (link)
Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao, “Causal Discovery with Cascade Nonlinear Additive Noise Model,” in Proceedings of International Joint Conference on Artificial Intelligence (IJCAI) 2019
Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao, “Learning Disentangled Semantic Representation for Domain Adaptation,” in Proceedings of International Joint Conference on Artificial Intelligence (IJCAI) 2019
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, Kun Zhang, Dacheng Tao, “Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping,” in Proceedings of The Conference on Computer Vision and Pattern Recognition (CVPR) 2019 Best Paper Finalist (link)
Ruibo Tu, Cheng Zhang, Paul Ackermann, Karthika Mohan, Clark Glymour, Hedvig Kjellström, Kun Zhang, “Causal discovery in the presence of missing data,” in Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS) 2019 (link)
Petar Stojanov, Mingming Gong, Jaime Carbonell , Kun Zhang, “Low-Dimensional Density Ratio Estimation for Covariate Shift Correction,” in Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS) 2019 (link)
Petar Stojanov, Mingming Gong, Jaime Carbonell, Kun Zhang, “Data-Driven Approach to Multiple-Source Domain Adaptation,” in Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS) 2019 (link)
AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang, “Counting and Sampling from Markov Equivalent DAGs Using Clique Trees,” In Proceedings of AAAI 2019 (link)
Kun Zhang, Bernhard Schölkopf, Peter Spirtes, Clark Glymour, “Learning Causality and Causality-Related Learning,” National Science Review, January 2018 (link)
Kun Zhang and Madelyn Glymour, "Unmixing for Causal Inference: Thoughts on McCaffrey and Danks," The British Journal for the Philosophy of Science, 2018 (link)
Menghan Wang, Mingming Gong, Xiaolin Zheng, Kun Zhang, “Modeling Dynamic Missingness of Implicit Feedback for Recommendation,” In Advances in Neural Information Processing Systems 32 (NeurIPS 2018), 2018 (link)
AmirEmad Ghassami, Biwei Huang, Negar Kiyavash, Kun Zhang, “Multi-Domain Causal Structure Learning in Linear Systems,” In Advances in Neural Information Processing Systems 32 (NeurIPS 2018), 2018 (link)
Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao, “Causal Discovery for Discrete Variables with Hidden Compact Representations,” In Advances in Neural Information Processing Systems 32 (NeurIPS 2018), 2018 (link)
Ya Li, Xinmei Tian, Mingming Gong, Yajing Liu, Tongliang Liu, Kun Zhang, Dacheng Tao, “Deep Domain Generalization via Conditional Invariant Adversarial Networks,” In Proceedings of European Conference on Computer Vision (ECCV) 2018 (link)
Kun Zhang, Mingming Gong, Joseph Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour, “Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results,” in Proc. Conference on Uncertainty in Artificial Intelligence (UAI’18), CA, USA, August, 2018 (plenary session) (link)
Biwei Huang, Kun Zhang, Yizhu Lin, Bernhard Schölkopf, and Clark Glymur, “Generalized Score Functions for Causal Discovery,” in Proc. ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD2018), London, August 2018 (long presentation) (link)
Saber SalehKaleyber, Jalal Etesami, Negar Kiyavash, and Kun Zhang, “Learning Vector Autoregressive Models with Latent Processes,” in Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), 2018 (link)
Menghan Wang, Xiaolin Zheng, Yang Yang, and Kun Zhang. Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), 2018 (link)
Peter Spirtes and Kun Zhang, “Search for Causal Models,” In Handbook of Graphical Models, Chapman & Hall/CRC Handbooks of Modern Statistical Methods series, (Ed) M. Drton, S. Lauritzen, M. Wainwright, and M. Maathuis, 2018 (link)
Heng Peng, Tao Huang, and Kun Zhang, “Model Selection for Gaussian Mixture Models," Statistica Sinica, 2017 (link)
Aditya Menon, Chetali Gupta, Kedar M. Perkins, Brian L. DeCost, Nikita Budwal, Renee T. Rios, Kun Zhang, Barnabás Póczosd and Newell R. Washburn, “Elucidating multi-physics interactions in suspensions for the design of polymeric dispersants: a hierarchical machine learning approach,” Molecular Systems Design & Engineering, 2017 (link)
AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang, “Learning Causal Structures Using Regression Invariance,” accepted to Advances in Neural Information Processing Systems 31 (NIPS 2017), 2017 (link)
Biwei Huang, Kun, Zhang, Jiji Zhang, Ruben Sanchez Romero, Clark Glymour, Bernhard Schölkopf, “Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows,” in Proceedings of IEEE 17th International Conference on Data Mining (ICDM 2017), 2017 (link)
Mingming Gong, Kun Zhang, Bernhard Schölkopf, Clark Glymour, and Dacheng Tao, “Causal Discovery from Temporally Aggregated Time Series,” in Proc. Conference on Uncertainty in Artificial Intelligence (UAI’17), Sydney, Australia, August 11-15, 2017 (link)
Kun Zhang, Mingming Gong, Joseph Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour, “Causal Discovery in the Presence of Measurement Error: Identifiability Conditions,” UAI 2017 Workshop on Causality: Learning, Inference, and Decision-Making, Sydney, Australia, August 15, 2017 (link)
M. Klasson, Kun Zhang, B. Bertilson, C. Zhang and H. Kjellström, Causality Refined Diagnostic Prediction, NIPS Workshop on Machine Learning for Health (ML4H), 2017
Kun Zhang, Biwei Huang, Jiji Zhang, Clark Glymour, Bernhard Schölkopf, "Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination," in Proc. International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne, Australia, August, 2017 (link)
Hao Zhang, Shuigeng Zhou, Kun Zhang, Jihong Guan, “Causal Discovery Using Regression-Based Conditional Independence Tests,” in Proc. 31th AAAI Conference on Artificial Intelligence (AAAI 2017), San Franceso, USA, Feb., 2017
Peter Spirtes and Kun Zhang, “Causal discovery and inference: Concepts and recent methodological advances,” Applied Informatics, 3(3), 2016 (link)
Kun Zhang, Zhikun Wang, Jiji Zhang, and Bernhard Schölkopf, “On estimation of functional causal models: Post-nonlinear causal model as an example,” ACM Transactions on Intelligent Systems and Technologies, 7(2), 2016 (link)
Kun Zhang, Jiji Zhang, Biwei Huang, Bernhard Schölkopf, Clark Glymour, “On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection,” Proceedings of the 32rd Conference on Uncertainty in Artificial Intelligence (UAI 2016), 2016 (plenary talk, acceptance rate ~ 9%) (link)
Jalal Etesam, Negar Kiyavash, Kun Zhang, and Kushagra Singhal, “Learning Causal Interaction Network of Multivariate Hawkes Processes,” Proceedings of the 32rd Conference on Uncertainty in Artificial Intelligence (UAI 2016), 2016 (link)
Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, and Bernhard Schölkopf, “Domain Adaptation with Conditional Transferable Components,” Proceedings of the 33nd International Conference on Machine Learning (ICML 2016), 2016 (link)
Kun Zhang and Aapo Hyvärinen, “Nonlinear functional causal models for distinguishing cause from effect,” in Statistics and Causality: Methods for Applied Empirical Research, (Ed) W Wiedemann and A von Eye, August, 2016
Jiji Zhang and Kun Zhang, “Likelihood and Consilience: On Forster's Counterexamples to the Likelihood Theory of Evidence,” Philosophy of Science, Supplementary Volume 2015
Philipp Geiger, Kun Zhang, Bernhard Schölkopf, Mingming Gong, and Dominik Janzing, “Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components,” In Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), (Ed) F. Bach and D. Blei, 37, JMLR W&CP, 1917–1925, Lille, France, July 2015 (link)
Mingming Gong*, Kun Zhang*, Bernhard Schölkopf, Dacheng Tao, and Philipp Geiger, “Discovering Temporal Causal Relations from Subsampled Data,” In Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), (Ed) F. Bach and D. Blei, 37, JMLR W&CP, 1898–1906, Lille, France, July 2015 (link)
Kun Zhang, Jiji Zhang and Bernhard Schölkopf, "Distinguishing Cause from Effect Based on Exogeneity," In Proc. Fifteenth Conference on Theoretical Aspects of Rationality and Knowledge, (TARK 2015), Pittsburgh, PA, June 2015 (link)
Peter Spirtes and Kun Zhang. Recent Methodological Advances in Causal Discovery and Inference In Proc. 15th Conference on Theoretical Aspects of Rationality and Knowledge (TARK 2015), Invited paper, Pittsburgh, PA, June 2015
Biwei Huang, Kun Zhang and Bernhard Schölkopf, “Time-dependent causal modeling: A Gaussian process treatment,” In Proc. 24th International Joint Conference on Artificial Intelligence (IJCAI’15), Machine Learning Track, Argentina, July 2015 (link)
Kun Zhang, Mingming Gong and Bernhard Schölkopf, “Domain adaptation with multiple sources: A Causal view,” in Proc. 29th AAAI Conference on Artificial Intelligence (AAAI 2015), Austin Texas, USA, Jan., 2015 (link)
Zhitang Chen, Kun Zhang, Laiwan Chan, and Bernhard Schölkopf, “Causal discovery via reproducing kernel Hilbert space embeddings,” Neural Computation, 26(7):1484-517, 2014
Gary Doran, Krikamol Muandet, Kun Zhang, and Bernhard Schölkopf, “A permutation-based kernel conditional independence test,” in Proc. 30th Uncertainty in Artificial Intelligence (UAI 2014), Canada, July 2014 (plenary talk, acceptance rate ~ 10%)
Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, Zhikun Wang, Zhi-Hua Zhou, and Claudio Persello, “Single-source domain adaptation with target and conditional shift,” in Regularization, Optimization, Kernels, and Support Vector Machines, J. A. K. Suykens, M. Signoretto, and Andreas Argyriou (Editors), 2014
Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, and Zhikun Wang, "Domain adaptation under target and conditional shift,” Proc. 29th International Conference on Machine Learning (ICML 2013), Atlanta, USA (full oral presentation; acceptance rate ~ 12%) (link)
Kun Zhang and Zhikun Wang, “On estimation of functional causal models: Post-nonlinear causal model as an example,” First IEEE/ICDM workshop on causal discovery, Dallas, USA, Dec., 2013
Zhitang Chen, Kun Zhang, and Laiwan Chan, "Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method," 2013 IEEE International Conference on Data Mining (ICDM'13), Dallas, USA, Dec., 2013 (acceptance rate ~ 19.7%)
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij, "Semi-supervised learning in causal and anticausal settings,” in Festschrift for Vladimir Vapnik's 75th birthday, 2013
Dominik Janzing, Joris Mooij, Kun Zhang, Jan Lemeire, Jakob Zscheischler, Povilas Daniuvsis, Bastian Steudel, Bernhard Schölkopf, “Information-geometric approach to inferring causal directions,” Artificial Intelligence, pp. 1-31, 2012 (link)
Zhitang Chen, Kun Zhang and Laiwan Chan, “Causal discovery with scale-mixture model for spatiotemporal variance dependencies,” in Advances in Neural Information Processing Systems 25 (NIPS 2012), Lake Tahoe, Nevada, United States (acceptance rate ~ 25.2%)
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij, "On causal and anticausal learning,” in Proc. 29th International Conference on Machine Learning (ICML 2012), Edinburgh, Scotland, June 2012 (acceptance rate ~ 27.3%) (link)
Kun Zhang, Jonas Peters, Dominik Janzing, and Bernhard Schölkopf, "Kernel-based conditional independence test and application in causal discovery,” in Proc. 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), Barcelona, Spain, July 2011 (acceptance rate ~ 30%) (link)
Jakob Zscheischler, Dominik Janzing, and Kun Zhang, "Testing whether linear equations are causal: A free probability theory approach,” in Proc. 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), Barcelona, Spain, July 2011 (acceptance rate ~ 30%) (link)
Kun Zhang and Aapo Hyvärinen, "A general linear non-Gaussian state-space model: Identifiability, identification, and application,” in Proc. 3rd Asian Conference on Machine Learning (ACML 2011), Taoyuan, Taiwan, Nov. 2011 (acceptance rate ~ 38%) (link)
Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, and Patrik Hoyer, "Estimation of a structural vector autoregression model using non-Gaussianity," Journal of Machine Learning Research, 11, pp. 1709-1731, 2010 (link)
Kun Zhang and Lai-Wan Chan, "Convolutive blind source separation by efficient blind deconvolution and minimal filter distortion," Neurocomputing, 73(13-15) 2580-2588, 2010
Joris Mooij, Oliver Stegle, Dominik Janzing, Kun Zhang, and Bernhard Schölkopf, “Probabilistic latent variable models for distinguishing between cause and effect,” in Advances in Neural Information Processing Systems 23, (NIPS 2010), Curran, NY, USA, 1687-1695 (acceptance rate ~ 30%) (link)
Kun Zhang, Bernhard Schölkopf, and Dominik Janzing, "Invariant gaussian process latent variable models and application in causal discovery,” in Proc. 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), California, July 2010 (plenary talk, acceptance rate ~ 10%) (link)
Kun Zhang and Aapo Hyvärinen, "Source separation and higher-order causal analysis of MEG and EEG,” in Proc. 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), California, July 2010 (acceptance rate ~ 30%) (link)
Povilas Daniusis, Dominik Janzing, Joris Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf, "Inferring deterministic causal relations,” in Proc. 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), California, July 2010 (plenary talk, acceptance rate ~ 10%) Best Student Paper Award (link)
Min-Ling Zhang and Kun Zhang, "Multi-label learning by exploiting label dependency,” in Proc. ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD2010), Washington DC, July 2010 (acceptance rate ~ 17%) (link)
Kun Zhang and Aapo Hyvärinen, "Distinguishing causes from effects using nonlinear acyclic causal models,” in JMLR Workshop and Conference Proceedings, Volume 6, pp. 157-164, 2010 (presented at NIPS 2008 workshop on causality) Best Benchmark Award (link)
Kun Zhang and Lai-Wan Chan, "Efficient factor GARCH models and factor-DCC models," Quantitative Finance , 9(1), pp. 71--91, 2009 (link)
Kun Zhang and Aapo Hyvärinen, "Acyclic causality discovery with additive noise: An information-theoretical perspective,” in Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2009, Bled, Slovenia, pp. 570--585, 2009 (acceptance rate ~ 25%) (link)
Kun Zhang and Aapo Hyvärinen, "On the identifiability of the post-nonlinear causal model,” in Proc. 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009), Montreal, Canada, 2009 (plenary talk, acceptance rate ~ 10%) (link)
Kun Zhang, Heng Peng, Laiwan Chan, and Aapo Hyvärinen, "ICA with sparse connections: Revisited,” in Proc. 8th Int. Conference on Independent Component Analysis and Signal Separation (ICA 2009), Paraty, Brazil, pp. 195--202, 2009 (link)
Kun Zhang and Lai-Wan Chan, "Minimal nonlinear distortion principle for nonlinear ICA," Journal of Machine Learning Research, 9, pp. 2455--2487, 2008
Kun Zhang and Lai-Wan Chan,"Separating convolutive mixtures by pairwise mutual information minimization,” IEEE Signal Processing Letters, 14(12), pp. 992--995, 2007
Wan Zhang, Liu Wenyin, and Kun Zhang, "Symbol recognition with kernel density matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), pp. 2020--2024, 2006
Kun Zhang and Lai-Wan Chan, "An adaptive method for subband decomposition ICA,” Neural Computation, 18(1), pp. 191--223, 2006
Kun Zhang and Lai-Wan Chan, "Dimension reduction as a deflation method in ICA,” IEEE Signal Processing Letters, 13(1), pp. 45--48, 2006
Kun Zhang and Lai-Wan Chan, "Extended Gaussianization method for blind separation of post-nonlinear mixtures,” Neural Computation, 17(2), pp. 425--452, 2006
Kun Zhang and Laiwan Chan, "Kernel-based nonlinear independent component analysis,” in Proc. 7th Int. Conference on Independent Component Analysis and Signal Separation (ICA 2007), London, UK, pp. 301--308, Sept., 2007
Kun Zhang and Laiwan Chan, "Nonlinear independent component analysis with minimum nonlinear distortion,” the 24th Annual International Conference on Machine Learning (ICML 2007), Corvallis, OR, US, pp. 1127--1134, Jun., 2007
Jian Li, Kun Zhang, and Laiwan Chan, "Portfolio construction by reinforcement learning of independent factors,” in Proc. of the 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2007), pp. 1020--1031, 2007
Kun Zhang and Lai-Wan Chan, "Enhancement of source independence for blind source separation,” in Proc. 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2006), LNCS 3889, Charleston, SC, USA, pp. 731--738, Mar., 2006
Kun Zhang and Lai-Wan Chan, "ICA by PCA approach: relating higher-order statistics to second-order moments,” in Proc. 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2006), LNCS 3889, Charleston, SC, USA, pp. 311--318, Mar., 2006
Kun Zhang and Lai-Wan Chan, "Extensions of ICA for causality discovery in the Hong Kong stock market,” in Proc. 13th International Conference on Neural Information Processing (ICONIP 2006), Hong Kong, Oct., 2006 (link)
Kun Zhang and Lai-Wan Chan, "ICA with sparse connections,” in Proc. 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2006), Burgos, Spain, pp. 530--537, Sep., 2006
Kun Zhang and Lai-Wan Chan, "To apply score function difference based ICA algorithms to high-dimensional data,” in Proc. of the 13th European Symposium on Artificial Neural Networks, Bruges, Belgium, pp. 291--297, 2005
Kun Zhang and Lai-Wan Chan, "Practical method for blind inversion of Wiener systems,” in Proc. of the International Joint Conference on Neural Networks (IJCNN'04), Budapest, Hungary, pp. 2163--2168, 2004
Kun Zhang and Lai-Wan Chan, "Dimension reduction based on orthogonality -- A decorrelation method in ICA,” in Artificial Neural Networks and Neural Information Processing, LNCS 2714, Springer-Verlag, Istanbul, Turkey, pp. 132--139, 2003