Publications

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

2022

  1. 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
  2. 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
  3. ICLR
    Optimal transport for causal discovery
    Tu, Ruibo, Zhang, Kun, Kjellström, Hedvig, and Zhang, Cheng
    In International Conference on Learning Representations (Spotlight) 2022
  4. 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
  5. 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
  6. CLeaR
    Attainability and Optimality: The Equalized-Odds Fairness Revisited
    Tang, Zeyu, and Zhang, Kun
    In the first Conference on Causal Learning and Reasoning 2022
  7. 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
  8. AIStats
    Towards Federated Bayesian Network Structure Learning with Continuous Optimization
    Ng, Ignavier, and Zhang, Kun
    In International Conference on Artificial Intelligence and Statistics 2022
  9. Pattern Recognit
    Relevance attack on detectors
    Chen, Sizhe, He, Fan, Huang, Xiaolin, and Zhang, Kun
    Pattern Recognition 2022
  10. 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
  11. 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
  12. 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

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
  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
  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
  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
  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
  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
  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
  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
  9. Theoria
    Computational Causal Discovery: Advantages and Assumptions (Commentary on James Woodward’s paper "Flagpoles anyone?: Causal and explanatory asymmetries")
    Zhang, Kun
    Theoria 2021
  10. Neural Netw
    Adversarial orthogonal regression: Two non-linear regressions for causal inference
    Heydari, M Reza, Salehkaleybar, Saber, and Zhang, Kun
    Neural Networks 2021
  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
  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
  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
  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

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
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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

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
  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
  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
  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
  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
  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
  7. Front Genet
    Review of causal discovery methods based on graphical models
    Glymour, Clark, Zhang, Kun, and Spirtes, Peter
    Frontiers in genetics 2019
  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
  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
  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
  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
  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
  13. UAI
    Domain generalization via multidomain discriminant analysis
    Hu, Shoubo, Zhang, Kun, Chen, Zhitang, and Chan, Laiwan
    In Uncertainty in Artificial Intelligence 2019
  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
  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
  16. IJCAI
    Causal discovery with cascade nonlinear additive noise models
    Cai, Ruichu, Qiao, Jie, Zhang, Kun, Zhang, Zhenjie, and Hao, Zhifeng
    In 2019
  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
  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
  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
  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
  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
  22. AAAI
    Counting and sampling from Markov equivalent DAGs using clique trees
    Ghassami, AmirEmad, Salehkaleybar, Saber, Kiyavash, Negar, and Zhang, Kun
    In Proceedings of the AAAI conference on Artificial Intelligence 2019