Publications

You may also checkout publications before 2019 and our research overview.

2024

  1. NeurIPS
    Identifying Selections for Unsupervised Subtask Discovery
    Qiu, Yiwen, Zheng, Yujia, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2024
    PDF
  2. NeurIPS
    On the Parameter Identifiability of Partially Observed Linear Causal Models
    Dong, Xinshuai, Ng, Ignavier, Huang, Biwei, Sun, Yuewen, Jin, Songyao, Legaspi, Roberto, Spirtes, Peter, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2024
    PDF
  3. NeurIPS
    On Causal Discovery in the Presence of Deterministic Relations
    Li, Longkang, Dai, Haoyue, Ghothani, Hanin Al, Huang, Biwei, Zhang, Jiji, Harel, Shahar, Bentwich, Isaac, Chen, Guangyi, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2024
    PDF
  4. NeurIPS
    Learning Discrete Concepts in Latent Hierarchical Models
    Kong, Lingjing, Chen, Guangyi, Huang, Biwei, Xing, Eric, Chi, Yuejie, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2024
    PDF
  5. NeurIPS
    Towards Understanding Extrapolation: a Causal Lens
    Kong, Lingjing, Chen, Guangyi, Stojanov, Petar, Li, Haoxuan, Xing, Eric, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2024
    PDF
  6. NeurIPS
    Causal Temporal Representation Learning with Nonstationary Sparse Transition
    Song, Xiangchen, Li, Zijian, Chen, Guangyi, Zheng, Yujia, Fan, Yewen, Dong, Xinshuai, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2024
    PDF
  7. NeurIPS
    Natural Counterfactuals With Necessary Backtracking
    Hao, Guang-Yuan, Zhang, Jiji, Huang, Biwei, Wang, Hao, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2024
    PDF
  8. NeurIPS
    Identifying Latent State-Transition Processes for Individualized Reinforcement Learning
    Sun, Yuewen, Huang, Biwei, Yao, Yu, Zeng, Donghuo, Dong, Xinshuai, Jin, Songyao, Sun, Boyang, Legaspi, Roberto, Ikeda, Kazushi, Spirtes, Peter, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2024
    PDF
  9. NeurIPS
    Learning Discrete Latent Variable Structures with Tensor Rank Conditions
    Chen, Zhengming, Cai, Ruichu, Xie, Feng, Qiao, Jie, Wu, Anpeng, Li, Zijian, Hao, Zhifeng, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2024
    PDF
  10. NeurIPS
    A Local Method for Satisfying Interventional Fairness with Partially Known Causal Graphs
    Li, Haoxuan, Liu, Yue, Geng, Zhi, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2024
    PDF
  11. NeurIPS
    Discovery of the Hidden World with Large Language Models
    Liu, Chenxi, Chen, Yongqiang, Liu, Tongliang, Gong, Mingming, Cheng, James, Han, Bo, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2024
    PDF
  12. NeurIPS
    Neural Collapse Inspired Feature Alignment for Out-of-Distribution Generalization
    Chen, Zhikang, Zhang, Min, Cui, Sen, Li, Haoxuan, Niu, Gang, Gong, Mingming, Zhang, Changshui, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2024
    PDF
  13. ICML
    Causal Representation Learning from Multiple Distributions: A General Setting
    Zhang, Kun, Xie, Shaoan, Ng, Ignavier, and Zheng, Yujia
    In International Conference on Machine Learning 2024 2024
    PDF
  14. ICML
    On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data
    Fan, Shunxin, Gong, Mingming, and Zhang, Kun
    In International Conference on Machine Learning 2024
    PDF
  15. ICML
    CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process
    Chen, Guangyi, Shen, Yifan, Chen, Zhenhao, Song, Xiangchen, Sun, Yuewen, Yao, Weiran, Liu, Xiao, and Zhang, Kun
    In International Conference on Machine Learning 2024
    PDF
  16. ICML
    Detecting and Identifying Selection Structure in Sequential Data
    Zheng, Yujia, Tang, Zeyu, Qiu, Yiwen, Schölkopf, Bernhard, and Zhang, Kun
    In International Conference on Machine Learning 2024
    PDF
  17. ICML
    Score-Based Causal Discovery in the Presence of Causally-Related Latent Variables
    Ng, Ignavier, Dong, Xinshuai, Dai, Haoyue, Huang, Biwei, Spirtes, Peter, and Zhang, Kun
    In International Conference on Machine Learning 2024
    PDF
  18. ICML
    Empowering Graph Invariance Learning with Deep Spurious Infomax
    Yao, Tianjun, Chen, Yongqiang, Chen, Zhenhao, Hu, Kai, Shen, Zhiqiang, and Zhang, Kun
    In International Conference on Machine Learning 2024
    PDF
  19. ICML
    Optimal Kernel Choice for Score Function-based Causal Discovery
    Wang, Wenjie, Huang, Biwei, Liu, Feng, You, Xinge, Liu, Tongliang, Zhang, Kun, and Gong, Mingming
    In International Conference on Machine Learning 2024
    PDF
  20. Nat. Commun.
    Ozone as an environmental driver of influenza
    Guo, Fang, Zhang, Pei, Do, Vivian, Gerhardus, Andreas, Runge, Jakob, Zhang, Kun, Han, Zheshen, Deng, Shenxi, Lin, Hongli, Ali, Sheikh Taslim, Chen, Ruchong, Guo, Yuming, and Tian, Linwei
    2024
    PDF
  21. JMLR
    Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations
    Wang, Yuanyuan, Huang, Wei, Gong, Mingming, Geng, Xi, Liu, Tongliang, Zhang, Kun, and Tao, Dacheng
    2024
    PDF
  22. AISTATS
    Local Causal Discovery with Linear non-Gaussian Cyclic Models
    Dai, Haoyue, Ng, Ignavier, Zheng, Yujia, Gao, Zhengqing, and Zhang, Kun
    In International Conference on Artificial Intelligence and Statistics 2024
    PDF
  23. CLeaR
    Structure Learning with Continuous Optimization: A Sober Look and Beyond
    Ng, Ignavier, Huang, Biwei, and Zhang, Kun
    In Conference on Causal Learning and Reasoning (Best Paper Award) 2024
    PDF
  24. WWW
    MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification
    Yao, Tianjun, Sun, Jiaqi, Cao, Defu, Zhang, Kun, and Chen, Guangyi
    In The Web Conference 2024
    PDF
  25. PT
    Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome
    Zeng, Donghuo, Legaspi, Roberto, Sun, Yuewen, Dong, Xinshuai, Ikeda, Kazushi, Zhang, Kun, and Spirtes, Peter
    In Persuasive Technology (Best paper nominee) 2024
    PDF
  26. ICLR
    Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
    Dai, Haoyue, Ng, Ignavier, Luo, Gongxu, Spirtes, Peter, Stojanov, Petar, and Zhang, Kun
    In International Conference on Learning Representations (Oral) 2024
    PDF
  27. ICLR
    Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions
    Li, Xiu-Chuan, Zhang, Kun, and Liu, Tongliang
    In International Conference on Learning Representations 2024
    PDF
  28. ICLR
    Procedural Fairness Through Decoupling Objectionable Data Generating Components
    Tang, Zeyu, Wang, Jialu, Liu, Yang, Spirtes, Peter, and Zhang, Kun
    In International Conference on Learning Representations (Spotlight) 2024
    PDF
  29. ICLR
    LLCP: Learning Latent Causal Processes for Reasoning-based Video Question Answer
    Chen, Guangyi, Li, Yuke, Liu, Xiao, Li, Zijian, Suradi, Eman Al, Wei, Donglai, and Zhang, Kun
    In International Conference on Learning Representations 2024
    PDF
  30. ICLR
    Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability
    Jin, Songyao, Xie, Feng, Chen, Guangyi, Huang, Biwei, Chen, Zhengming, Dong, Xinshuai, and Zhang, Kun
    In International Conference on Learning Representations 2024
    PDF
  31. ICLR
    Federated Causal Discovery from Heterogeneous Data
    Li, Longkang, Ng, Ignavier, Luo, Gongxu, Huang, Biwei, Chen, Guangyi, Liu, Tongliang, Gu, Bin, and Zhang, Kun
    In International Conference on Learning Representations 2024
    PDF
  32. ICLR
    Identifiable Latent Polynomial Causal Models through the Lens of Change
    Liu, Yuhang, Zhang, Zhen, Gong, Dong, Gong, Mingming, Huang, Biwei, Hengel, Anton van den, Zhang, Kun, and Shi, Javen Qinfeng
    In International Conference on Learning Representations 2024
    PDF
  33. ICLR
    A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
    Dong, Xinshuai, Huang, Biwei, Ng, Ignavier, Song, Xiangchen, Zheng, Yujia, Jin, Songyao, Legaspi, Roberto, Spirtes, Peter, and Zhang, Kun
    In International Conference on Learning Representations 2024
    PDF
  34. AAAI
    ACAMDA: Improving Data Efficiency in Reinforcement Learning Through Guided Counterfactual Data Augmentation
    Sun, Yuewen, Wang, Erli, Huang, Biwei, Lu, Chaochao, Feng, Lu, Sun, Changyin, and Zhang, Kun
    In Proceedings of the AAAI conference on Artificial Intelligence 2024
    PDF
  35. AAAI
    Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment
    Zhang, Sheng, Naseer, Muzammal, Chen, Guangyi, Shen, Zhiqiang, Khan, Salman, Zhang, Kun, and Khan, Fahad Shahbaz
    In Proceedings of the AAAI conference on Artificial Intelligence 2024
    PDF
  36. AAAI
    Identification of Causal Structure with Latent Variables based on Higher Order Cumulants
    Chen, Wei, Huang, Zhiyi, Cai, Ruichu, Hao, Zhifeng, and Zhang, Kun
    In Proceedings of the AAAI conference on Artificial Intelligence 2024
    PDF

2023

  1. NeurIPS
    Generalizing Nonlinear ICA Beyond Structural Sparsity
    Zheng, Yujia, and Zhang, Kun
    In Conference on Neural Information Processing Systems (Oral) 2023
    PDF
  2. NeurIPS
    Learning World Models with Identifiable Factorization
    Liu, Yu-Ren, Huang, Biwei, Zhu, Zhengmao, Tian, Honglong, Gong, Mingming, Yu, Yang, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2023
    PDF
  3. NeurIPS
    Temporally Disentangled Representation Learning under Unknown Nonstationarity
    Song, Xiangchen, Yao, Weiran, Fan, Yewen, Dong, Xinshuai, Chen, Guangyi, Niebles, Juan Carlos, Xing, Eric, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2023
    PDF
  4. NeurIPS
    Counterfactual Generation with Identifiability Guarantee
    Yan, Hanqi, Kong, Lingjing, Gui, Lin, Chi, Yuejie, Xing, Eric, He, Yulan, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2023
    PDF
  5. NeurIPS
    Identification of Nonlinear Latent Hierarchical Models
    Kong, Lingjing, Huang, Biwei, Xie, Feng, Xing, Eric, Chi, Yuejie, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2023
    PDF
  6. NeurIPS
    On the Identifiability of Sparse ICA without Assuming Non-Gaussianity
    Ng, Ignavier, Zheng, Yujia, Dong, Xinshuai, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2023
    PDF
  7. NeurIPS
    Subspace Identification for Multi-Source Domain Adaptation
    Li, Zijian, Cai, Ruichu, Chen, Guangyi, Sun, Boyang, Hao, Zhifeng, and Zhang, Kun
    In Conference on Neural Information Processing Systems (Spotlight) 2023
    PDF
  8. ACM CSUR
    What-Is and How-To for Fairness in Machine Learning: A Survey, Reflection, and Perspective
    Tang, Zeyu, Zhang, Jiji, and Zhang, Kun
    ACM Computing Surveys 2023
    PDF
  9. ICML
    Identifiability of Label Noise Transition Matrix
    Liu, Yang, Cheng, Hao, and Zhang, Kun
    In International Conference on Machine Learning 2023
    PDF
  10. ICML
    Causal Discovery with Latent Confounders Based on Higher-Order Cumulants
    Cai, Ruichu, Huang, Zhiyi, Chen, Wei, Hao, Zhifeng, and Zhang, Kun
    In International Conference on Machine Learning 2023
    PDF
  11. ICML
    Evolving Semantic Prototype Improves Generative Zero-Shot Learning
    Chen, Shiming, Hou, Wenjin, Hong, Ziming, Ding, Xiaohan, Song, Yibing, You, Xinge, Liu, Tongliang, and Zhang, Kun
    In International Conference on Machine Learning 2023
    PDF
  12. ICML
    Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?” accepted to International Conference on Machine Learning 2023
    Yao, Yu, Gong, Mingming, Du, Yuxuan, Yu, Jun, Han, Bo, Zhang, Kun, and Liu, Tongliang
    In International Conference on Machine Learning 2023
    PDF
  13. ICML
    Feature Expansion for Graph Neural Networks
    Sun, Jiaqi, Zhang, Lin, Chen, Guangyi, XU, Peng, Zhang, Kun, and Yang, Yujiu
    In International Conference on Machine Learning 2023
    PDF
  14. ICML
    Model Transferability with Responsive Decision Subjects
    Chen, Yatong, Tang, Zeyu, Zhang, Kun, and Liu, Yang
    In International Conference on Machine Learning 2023
    PDF
  15. CVPR
    Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction
    Chen, Guangyi, Chen, Zhenhao, Fan, Shunxing, and Zhang, Kun
    In Proceedings of The Conference on Computer Vision and Pattern Recognition 2023
    PDF
  16. CVPR
    Understanding Masked Autoencoders via Hierarchical Latent Variable Models
    Kong, Lingjing, Ma, Martin Q., Chen, Guangyi, Xing, Eric, Chi, Yuejie, Morency, Louis-Philippe, and Zhang, Kun
    In Proceedings of The Conference on Computer Vision and Pattern Recognition 2023
    PDF
  17. CVPR
    Unpaired Image-to-Image Translation with Shortest Path Regularization
    Xie, Shaoan, xu, yanwu, Gong, Mingming, and Zhang, Kun
    In Proceedings of The Conference on Computer Vision and Pattern Recognition 2023
    PDF
  18. CVPR
    SmartBrush: Diffusion-based Multi-modal Image Inpainting
    Xie, Shaoan, Zhang, Zhifei, Lin, Zhe, Hinz, Tobias, and Zhang, Kun
    In Proceedings of The Conference on Computer Vision and Pattern Recognition 2023
    PDF
  19. ICLR
    Multi-domain image generation and translation with identifiability guarantees
    Xie, Shaoan, Kong, Lingjing, Gong, Mingming, and Zhang, Kun
    In International Conference on Learning Representations 2023
    PDF
  20. ICLR
    Scalable Estimation of Nonparametric Markov Networks with Mixed-Type Data
    Zheng, Yujia, Ng, Ignavier, Fan, Yewen, and Zhang, Kun
    In International Conference on Learning Representations 2023
    PDF
  21. ICLR
    Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors
    Tang, Zeyu, Chen, Yatong, Liu, Yang, and Zhang, Kun
    In International Conference on Learning Representations 2023
    PDF
  22. ICLR
    Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems
    Fan, Yewen, Si, Nian, and Zhang, Kun
    In International Conference on Learning Representations 2023
    PDF
  23. ICLR
    Prompt Learning with Optimal Transport for Vision-Language Models
    Chen, Guangyi, Yao, Weiran, Song, Xiangchen, Li, Xinyue, Rao, Yongming, and Zhang, Kun
    In International Conference on Learning Representations 2023
    PDF
  24. ICLR
    Causal Balancing for Domain Generalization
    Wang, Xinyi, Saxon, Michael, Li, Jiachen, Zhang, Hongyang, Zhang, Kun, and Wang, William Yang
    In International Conference on Learning Representations 2023
    PDF
  25. ICLR
    GAIN: On the Generalization of Instructional Action Understanding
    Li, Junlong, Chen, Guangyi, Tang, Yansong, Bao, Jinan, Zhang, Kun, Zhou, Jie, and Lu, Jiwen
    In International Conference on Learning Representations 2023
    PDF
  26. CLeaR
    Scalable Causal Discovery with Score Matching
    Montagna, Francesco, Noceti, Nicoletta, Rosasco, Lorenzo, Zhang, Kun, and Locatello, Francesco
    In the 2nd Conference on Causal Learning and Reasoning 2023
    PDF
  27. CLeaR
    Causal Discovery with Score Matching on Additive Models with Arbitrary Noise
    Montagna, Francesco, Noceti, Nicoletta, Rosasco, Lorenzo, Zhang, Kun, and Locatello, Francesco
    In the 2nd Conference on Causal Learning and Reasoning 2023
    PDF
  28. ISBI
    Deep DAG Learning of Effective Brain Connectivity for fMRI Analysis
    Yu, Yue, Kan, Xuan, Cui, Hejie, Xu, Ran, Zheng, Yujia, Song, Xiangchen, Zhu, Yanqiao, Zhang, Kun, Nabi, Razieh, Guo, Ying, Zhang, Chao, and Yang, Carl
    In IEEE International Symposium on Biomedical Imaging 2023
    PDF
  29. ISBI
    Knee Injury Diagnosis with Data and Feature Fusion-Enhanced Multi-label Classification Network
    Ghothani, Hanin Al, and Zhang, Kun
    In IEEE International Symposium on Biomedical Imaging 2023
    HTML

2022

  1. NeurIPS
    On the Identifiability of Nonlinear ICA: Sparsity and Beyond
    Zheng, Yujia, Ng, Ignavier, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2022
    PDF
  2. NeurIPS
    Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models
    Dai, Haoyue, Spirtes, Peter, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2022
    PDF
  3. NeurIPS
    Causal Disentanglement for Time Series
    Yao, Weiran, Chen, Guangyi, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2022
    PDF
  4. NeurIPS
    Unsupervised Image-to-Image Translation with Density Changing Regularization
    Xie, Shaoan, Ho, Qirong, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2022
    PDF
  5. NeurIPS
    Latent Hierarchical Causal Structure Discovery with Rank Constraints
    Huang, Biwei, Low, Charles, Xie, Feng, Glymour, Clark, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2022
    PDF
  6. NeurIPS
    Factored Adaptation for Non-Stationary Reinforcement Learning
    Feng, Fan, Huang, Biwei, Zhang, Kun, and Magliacane, Sara
    In Conference on Neural Information Processing Systems 2022
    PDF
  7. NeurIPS
    Causal Discovery in Linear Latent Variable Models Subject to Measurement Error
    Yang, Yuqin, Ghassami, AmirEmad, Nafea, Mohamed S, Kiyavash, Negar, Zhang, Kun, and Shpitser, Ilya
    In Conference on Neural Information Processing Systems 2022
    PDF
  8. NeurIPS
    Counterfactual Fairness with Partially Known Causal Graph
    Zuo, Aoqi, Wei, Susan, Liu, Tongliang, Han, Bo, Zhang, Kun, and Gong, Mingming
    In Conference on Neural Information Processing Systems 2022
    PDF
  9. NeurIPS
    MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models
    Gao, Erdun, Ng, Ignavier, Gong, Mingming, Shen, Li, Huang, Wei, Liu, Tongliang, Zhang, Kun, and Bondell, Howard
    In Conference on Neural Information Processing Systems 2022
    PDF
  10. NeurIPS
    Truncated Matrix Power Iteration for Differentiable DAG Learning
    Zhang, Zhen, Ng, Ignavier, Gong, Dong, Liu, Yuhang, Abbasnejad, Ehsan M, Gong, Mingming, Zhang, Kun, and Shi, Javen Qinfeng
    In Conference on Neural Information Processing Systems 2022
    PDF
  11. ICML
    Identification of Linear Non-Gaussian Latent Hierarchical Structure
    Xie, Feng, Huang, Biwei, Chen, Zhengming, He, Yangbo, Geng, Zhi, and Zhang, Kun
    In International Conference on Machine Learning 2022
    PDF
  12. ICML
    Partial disentanglement for domain adaptation
    Kong, Lingjing, Xie, Shaoan, Yao, Weiran, Zheng, Yujia, Chen, Guangyi, Stojanov, Petar, Akinwande, Victor, and Zhang, Kun
    In International Conference on Machine Learning 2022
    PDF
  13. ICML
    Action-sufficient state representation learning for control with structural constraints
    Huang, Biwei, Lu, Chaochao, Liu, Leqi, Hernández-Lobato, José Miguel, Glymour, Clark, Schölkopf, Bernhard, and Zhang, Kun
    In International Conference on Machine Learning 2022
    PDF
  14. CVPR
    Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation
    Xu, Yanwu, Xie, Shaoan, Wu, Wenhao, Zhang, Kun, Gong, Mingming, and Batmanghelich, Kayhan
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022
    PDF
  15. CVPR
    Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint
    Guo, Jiaxian, Li, Jiachen, Fu, Huan, Gong, Mingming, Zhang, Kun, and Tao, Dacheng
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022
    PDF
  16. ICLR
    AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
    Huang, Biwei, Feng, Fan, Lu, Chaochao, Magliacane, Sara, and Zhang, Kun
    In International Conference on Learning Representations (Spotlight) 2022
    PDF
  17. ICLR
    Learning Temporally Latent Causal Processes from General Temporal Data
    Yao, Weiran, Sun, Yuewen, Ho, Alex, Sun, Changyin, and Zhang, Kun
    In International Conference on Learning Representations 2022
    PDF
  18. ICLR
    Optimal transport for causal discovery
    Tu, Ruibo, Zhang, Kun, Kjellström, Hedvig, and Zhang, Cheng
    In International Conference on Learning Representations (Spotlight) 2022
    PDF
  19. ICLR
    Conditional contrastive learning: Removing undesirable information in self-supervised representations
    Tsai, Yao-Hung Hubert, Ma, Martin Q, Zhao, Han, Zhang, Kun, Morency, Louis-Philippe, and Salakhutdinov, Ruslan
    In International Conference on Learning Representations 2022
    PDF
  20. ICLR
    Adversarial robustness through the lens of causality
    Zhang, Yonggang, Gong, Mingming, Liu, Tongliang, Niu, Gang, Tian, Xinmei, Han, Bo, Schölkopf, Bernhard, and Zhang, Kun
    In International Conference on Learning Representations 2022
    PDF
  21. CLeaR
    Attainability and Optimality: The Equalized-Odds Fairness Revisited
    Tang, Zeyu, and Zhang, Kun
    In the first Conference on Causal Learning and Reasoning 2022
    PDF
  22. AIStats
    On the convergence of continuous constrained optimization for structure learning
    Ng, Ignavier, Lachapelle, Sébastien, Ke, Nan Rosemary, Lacoste-Julien, Simon, and Zhang, Kun
    In International Conference on Artificial Intelligence and Statistics 2022
    PDF
  23. AIStats
    Towards Federated Bayesian Network Structure Learning with Continuous Optimization
    Ng, Ignavier, and Zhang, Kun
    In International Conference on Artificial Intelligence and Statistics 2022
    PDF
  24. Pattern Recognit
    Relevance attack on detectors
    Chen, Sizhe, He, Fan, Huang, Xiaolin, and Zhang, Kun
    Pattern Recognition 2022
    PDF
  25. AAAI
    Invariant Action Effect Model for Reinforcement Learning
    Zhu, Zhengmao, Jiang, Shengyi, Liu, Yu-Ren, Yu, Yang, and Zhang, Kun
    In Proceedings of the AAAI conference on Artificial Intelligence 2022
    PDF
  26. AAAI
    Residual Similarity Based Conditional Independence Test and Its Application in Causal Discovery
    Zhang, Hao, Zhou, Shuigeng, Zhang, Kun, and Guan, Jihong
    In Proceedings of the AAAI conference on Artificial Intelligence 2022
    PDF
  27. AAAI
    Identification of Linear Latent Variable Model with Arbitrary Distribution
    Chen, Zhengming, Xie, Feng, Qiao, Jie, Hao, Zhifeng, Zhang, Kun, and Cai, Ruichu
    In Proceedings of the AAAI conference on Artificial Intelligence 2022
    PDF

2021

  1. NeurIPS
    Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?
    Stojanov, Petar, Li, Zijian, Gong, Mingming, Cai, Ruichu, Carbonell, Jaime, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2021
    PDF
  2. NeurIPS
    Identification of partially observed linear causal models: Graphical conditions for the non-gaussian and heterogeneous cases
    Adams, Jeffrey, Hansen, Niels, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2021
    PDF
  3. NeurIPS
    Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions
    Ng, Ignavier, Zheng, Yujia, Zhang, Jiji, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2021
    PDF
  4. NeurIPS
    Instance-dependent Label-noise Learning under a Structural Causal Model
    Yao, Yu, Liu, Tongliang, Gong, Mingming, Han, Bo, Niu, Gang, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2021
    PDF
  5. ICCV
    Unaligned image-to-image translation by learning to reweight
    Xie, Shaoan, Gong, Mingming, Xu, Yanwu, and Zhang, Kun
    In Proceedings of the International Conference on Computer Vision 2021
    PDF
  6. IJCAI
    Progressive open-domain response generation with multiple controllable attributes
    Yang, Haiqin, Yao, Xiaoyuan, Duan, Yiqun, Shen, Jianping, Zhong, Jie, and Zhang, Kun
    In Proceedings of the International Joint Conference on Artificial Intelligence 2021
    PDF
  7. TNNLS
    Model-Based Transfer Reinforcement Learning Based on Graphical Model Representations
    Sun, Yuewen, Zhang, Kun, and Sun, Changyin
    IEEE Transactions on Neural Networks and Learning Systems 2021
    PDF
  8. TIST
    Causal Discovery with Confounding Cascade Nonlinear Additive Noise Models
    Qiao, Jie, Cai, Ruichu, Zhang, Kun, Zhang, Zhenjie, and Hao, Zhifeng
    ACM Transactions on Intelligent Systems and Technology 2021
    PDF
  9. Theoria
    Computational Causal Discovery: Advantages and Assumptions (Commentary on James Woodward’s paper "Flagpoles anyone?: Causal and explanatory asymmetries")
    Zhang, Kun
    Theoria 2021
    PDF
  10. Neural Netw
    Adversarial orthogonal regression: Two non-linear regressions for causal inference
    Heydari, M Reza, Salehkaleybar, Saber, and Zhang, Kun
    Neural Networks 2021
    PDF
  11. AAAI
    Improving Causal Discovery By Optimal Bayesian Network Learning
    Lu, Ni, Zhang, Kun, and Yuan, Changhe
    In Proceedings of the AAAI conference on Artificial Intelligence 2021
    PDF
  12. AAAI
    DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding
    Wang, Zhicheng, Huang, Biwei, Tu, Shikui, Zhang, Kun, and Xu, Lei
    In Proceedings of the AAAI conference on Artificial Intelligence 2021
    PDF
  13. AAAI
    Testing Independence Between Linear Combinations for Causal Discovery
    Zhang, Hao, Zhang, Kun, Zhou, Shuigeng, Guan, Jihong, and Zhang, Ji
    In Proceedings of the AAAI Conference on Artificial Intelligence 2021
    PDF
  14. TNNLS
    Causal Discovery in Linear Non-Gaussian Acyclic Model With Multiple Latent Confounders
    Chen, Wei, Cai, Ruichu, Zhang, Kun, and Hao, Zhifeng
    IEEE Transactions on Neural Networks and Learning Systems 2021
    PDF

2020

  1. NeurIPS
    Domain adaptation as a problem of inference on graphical models
    Zhang, Kun, Gong, Mingming, Stojanov, Petar, Huang, Biwei, Liu, Qingsong, and Glymour, Clark
    In Conference on Neural Information Processing Systems 2020
    PDF
  2. NeurIPS
    Generalized independent noise condition for estimating latent variable causal graphs
    Xie, Feng, Cai, Ruichu, Huang, Biwei, Glymour, Clark, Hao, Zhifeng, and Zhang, Kun
    In Conference on Neural Information Processing Systems (Spotlight) 2020
    PDF
  3. NeurIPS
    On the role of sparsity and dag constraints for learning linear dags
    Ng, Ignavier, Ghassami, AmirEmad, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2020
    PDF
  4. NeurIPS
    How do fair decisions fare in long-term qualification?
    Zhang, Xueru, Tu, Ruibo, Liu, Yang, Liu, Mingyan, Kjellstrom, Hedvig, Zhang, Kun, and Zhang, Cheng
    In Conference on Neural Information Processing Systems 2020
    PDF
  5. NeurIPS
    A causal view on robustness of neural networks
    Zhang, Cheng, Zhang, Kun, and Li, Yingzhen
    In Conference on Neural Information Processing Systems 2020
    PDF
  6. Bioinformatics
    Unpaired data empowers association tests
    Gong, Mingming, Liu, Peng, Sciurba, Frank C, Stojanov, Petar, Tao, Dacheng, Tseng, George C, Zhang, Kun, and Batmanghelich, Kayhan
    Bioinformatics 2020
    PDF
  7. JMLR
    Causal Discovery from Heterogeneous/Nonstationary Data
    Huang, Biwei, Zhang, Kun, Zhang, Jiji, Ramsey, Joseph D, Sanchez-Romero, Ruben, Glymour, Clark, and Schölkopf, Bernhard
    Journal of Machine Learning Research 2020
    PDF
  8. ECCV
    Adaptive task sampling for meta-learning
    Liu, Chenghao, Wang, Zhihao, Sahoo, Doyen, Fang, Yuan, Zhang, Kun, and Hoi, Steven CH
    In European Conference on Computer Vision 2020
    PDF
  9. ICML
    Characterizing Distribution Equivalence for Cyclic and Acyclic Directed Graphs
    Ghassami, AmirEmad, Zhang, Kun, and Kiyavash, Negar
    In International conference on machine learning 2020
    PDF
  10. ICML
    Ltf: A label transformation framework for correcting label shift
    Guo, Jiaxian, Gong, Mingming, Liu, Tongliang, Zhang, Kun, and Tao, Dacheng
    In International Conference on Machine Learning 2020
    PDF
  11. ICML
    Label-noise robust domain adaptation
    Yu, Xiyu, Liu, Tongliang, Gong, Mingming, Zhang, Kun, Batmanghelich, Kayhan, and Tao, Dacheng
    In International Conference on Machine Learning 2020
    PDF
  12. JMLR
    Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables.
    Salehkaleybar, Saber, Ghassami, AmirEmad, Kiyavash, Negar, and Zhang, Kun
    Journal of Machine Learning Research 2020
    PDF
  13. tmc
    Transfer Learning-Based Outdoor Position Recovery With Cellular Data
    Zhang, Yige, Ding, Aaron Yi, Ott, Jörg, Yuan, Mingxuan, Zeng, Jia, Zhang, Kun, and Rao, Weixiong
    IEEE Transactions on Mobile Computing 2020
    PDF
  14. AAAI
    Causal discovery from multiple data sets with non-identical variable sets
    Huang, Biwei, Zhang, Kun, Gong, Mingming, and Glymour, Clark
    In Proceedings of the AAAI conference on Artificial Intelligence 2020
    PDF
  15. AAAI
    Generative-discriminative complementary learning
    Xu, Yanwu, Gong, Mingming, Chen, Junxiang, Liu, Tongliang, Zhang, Kun, and Batmanghelich, Kayhan
    In Proceedings of the AAAI Conference on Artificial Intelligence 2020
    PDF
  16. AAAI
    Compressed Self-Attention for Deep Metric Learning
    Chen, Ziye, Gong, Mingming, Xu, Yanwu, Wang, Chaohui, Zhang, Kun, and Du, Bo
    In Proceedings of the AAAI Conference on Artificial Intelligence 2020
    PDF

2019

  1. NeurIPS
    Specific and shared causal relation modeling and mechanism-based clustering
    Huang, Biwei, Zhang, Kun, Xie, Pengtao, Gong, Mingming, Xing, Eric P, and Glymour, Clark
    In Conference on Neural Information Processing Systems 2019
    PDF
  2. NeurIPS
    Triad constraints for learning causal structure of latent variables
    Cai, Ruichu, Xie, Feng, Glymour, Clark, Hao, Zhifeng, and Zhang, Kun
    In Conference on Neural Information Processing Systems 2019
    PDF
  3. NeurIPS
    Twin auxilary classifiers gan
    Gong, Mingming, Xu, Yanwu, Li, Chunyuan, Zhang, Kun, and Batmanghelich, Kayhan
    In Conference on Neural Information Processing Systems 2019
    PDF
  4. NeurIPS
    Likelihood-free overcomplete ICA and applications in causal discovery
    Ding, Chenwei, Gong, Mingming, Zhang, Kun, and Tao, Dacheng
    In Conference on Neural Information Processing Systems 2019
    PDF
  5. NeurIPS
    Neuropathic pain diagnosis simulator for causal discovery algorithm evaluation
    Tu, Ruibo, Zhang, Kun, Bertilson, Bo, Kjellstrom, Hedvig, and Zhang, Cheng
    In Conference on Neural Information Processing Systems 2019
    PDF
  6. CIKM
    Prnet: Outdoor position recovery for heterogenous telco data by deep neural network
    Zhang, Yige, Rao, Weixiong, Zhang, Kun, Yuan, Mingxuan, and Zeng, Jia
    In Proceedings of the ACM International Conference on Information and Knowledge Management 2019
    PDF
  7. Front Genet
    Review of causal discovery methods based on graphical models
    Glymour, Clark, Zhang, Kun, and Spirtes, Peter
    Frontiers in genetics 2019
    PDF
  8. Nat. Commun.
    Inferring causation from time series in Earth system sciences
    Runge, Jakob, Bathiany, Sebastian, Bollt, Erik, Camps-Valls, Gustau, Coumou, Dim, Deyle, Ethan, Glymour, Clark, Kretschmer, Marlene, Mahecha, Miguel D, Muñoz-Marı́, Jordi, and others,
    Nature communications 2019
    PDF
  9. Netw
    Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods
    Sanchez-Romero, Ruben, Ramsey, Joseph D, Zhang, Kun, Glymour, Madelyn RK, Huang, Biwei, and Glymour, Clark
    Network Neuroscience 2019
    PDF
  10. Open Philos
    The evaluation of discovery: Models, simulation and search through “big data”
    Glymour, Clark, Ramsey, Joseph D, and Zhang, Kun
    Open Philosophy 2019
    PDF
  11. J. Causal Inference
    Approximate kernel-based conditional independence tests for fast non-parametric causal discovery
    Strobl, Eric V, Zhang, Kun, and Visweswaran, Shyam
    Journal of Causal Inference 2019
    PDF
  12. UAI
    Causal discovery with general non-linear relationships using non-linear ica
    Monti, Ricardo Pio, Zhang, Kun, and Hyvärinen, Aapo
    In Uncertainty in Artificial Intelligence 2019
    PDF
  13. UAI
    Domain generalization via multidomain discriminant analysis
    Hu, Shoubo, Zhang, Kun, Chen, Zhitang, and Chan, Laiwan
    In Uncertainty in Artificial Intelligence 2019
    PDF
  14. ICML
    Causal discovery and forecasting in nonstationary environments with state-space models
    Huang, Biwei, Zhang, Kun, Gong, Mingming, and Glymour, Clark
    In International conference on machine learning 2019
    PDF
  15. ICML
    On learning invariant representations for domain adaptation
    Zhao, Han, Des Combes, Remi Tachet, Zhang, Kun, and Gordon, Geoffrey
    In International Conference on Machine Learning 2019
    PDF
  16. IJCAI
    Causal discovery with cascade nonlinear additive noise models
    Cai, Ruichu, Qiao, Jie, Zhang, Kun, Zhang, Zhenjie, and Hao, Zhifeng
    In 2019
    PDF
  17. IJCAI
    Learning disentangled semantic representation for domain adaptation
    Cai, Ruichu, Li, Zijian, Wei, Pengfei, Qiao, Jie, Zhang, Kun, and Hao, Zhifeng
    In Proceedings of the International Joint Conference on Artificial Intelligence 2019
    PDF
  18. CVPR
    Geometry-consistent generative adversarial networks for one-sided unsupervised domain mapping
    Fu, Huan, Gong, Mingming, Wang, Chaohui, Batmanghelich, Kayhan, Zhang, Kun, and Tao, Dacheng
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (Best Paper Finalist) 2019
    PDF
  19. AIStats
    Low-dimensional density ratio estimation for covariate shift correction
    Stojanov, Petar, Gong, Mingming, Carbonell, Jaime, and Zhang, Kun
    In International Conference on Artificial Intelligence and Statistics 2019
    PDF
  20. AIStats
    Causal discovery in the presence of missing data
    Tu, Ruibo, Zhang, Cheng, Ackermann, Paul, Mohan, Karthika, Kjellström, Hedvig, and Zhang, Kun
    In International Conference on Artificial Intelligence and Statistics 2019
    PDF
  21. AIStats
    Data-driven approach to multiple-source domain adaptation
    Stojanov, Petar, Gong, Mingming, Carbonell, Jaime, and Zhang, Kun
    In International Conference on Artificial Intelligence and Statistics 2019
    PDF