Publications

2025

  1. ICML
    A General Representation-Based Approach to Multi-Source Domain Adaptation
    Ignavier Ng, Yan Li, Zijian Li, Yujia Zheng, Guangyi Chen, Kun Zhang
    International Conference on Machine Learning, 2025
    PDF
  2. ICML
    Permutation-based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data
    Xinshuai Dong, Ignavier Ng, Boyang Sun, Haoyue Dai, Guang-Yuan Hao, Shunxing Fan, Peter Spirtes, Yumou Qiu, Kun Zhang
    International Conference on Machine Learning, 2025
    PDF
  3. ICML
    Latent Variable Causal Discovery under Selection Bias
    Haoyue Dai, Yiwen Qiu, Ignavier Ng, Xinshuai Dong, Peter Spirtes, Kun Zhang
    International Conference on Machine Learning, 2025
    PDF
  4. AISTATS
    Causal Representation Learning from General Environments under Nonparametric Mixing
    Ignavier Ng, Shaoan Xie, Xinshuai Dong, Peter Spirtes, Kun Zhang
    International Conference on Artificial Intelligence and Statistics, 2025
    PDF
  5. ICLR Oral
    When Selection Meets Intervention: Additional Complexities in Causal Discovery
    Haoyue Dai, Ignavier Ng, Jianle Sun, Zeyu Tang, Gongxu Luo, Xinshuai Dong, Peter Spirtes, Kun Zhang
    International Conference on Learning Representations, 2025
    PDF
  6. ICLR
    Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning
    Zijian Li, Shunxing Fan, Yujia Zheng, Ignavier Ng, Shaoan Xie, Guangyi Chen, Xinshuai Dong, Ruichu Cai, Kun Zhang
    International Conference on Learning Representations, 2025
    PDF
  7. ICLR
    Differentiable Causal Discovery for Latent Hierarchical Causal Models
    Parjanya Prajakta Prashant, Ignavier Ng, Kun Zhang, Biwei Huang
    International Conference on Learning Representations, 2025
    PDF
  8. ICLR
    Analytic DAG Constraints for Differentiable DAG Learning
    Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Mingming Gong, Biwei Huang, Kun Zhang, Anton van den Hengel, Javen Qinfeng Shi
    International Conference on Learning Representations, 2025
    PDF
  9. ICLR
    A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery
    Yingyu Lin, Yuxing Huang, Wenqin Liu, Haoran Deng, Ignavier Ng, Kun Zhang, Mingming Gong, Yian Ma, Biwei Huang
    International Conference on Learning Representations, 2025
    PDF

2024

  1. NeurIPS
    On the Parameter Identifiability of Partially Observed Linear Causal Models
    Xinshuai Dong, Ignavier Ng, Biwei Huang, Yuewen Sun, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang
    Advances in Neural Information Processing Systems, 2024
    PDF
  2. ICML
    Score-Based Causal Discovery of Latent Variable Causal Models
    Ignavier Ng, Xinshuai Dong, Haoyue Dai, Biwei Huang, Peter Spirtes, Kun Zhang
    International Conference on Machine Learning, 2024
    PDF
  3. ICML
    Causal Representation Learning from Multiple Distributions: A General Setting
    Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng
    International Conference on Machine Learning, 2024
    PDF
  4. CLeaR Best Paper Award
    Structure Learning with Continuous Optimization: A Sober Look and Beyond
    Ignavier Ng, Biwei Huang, Kun Zhang
    Conference on Causal Learning and Reasoning, 2024
    PDF
  5. AISTATS
    Local Causal Discovery with Linear non-Gaussian Cyclic Models
    Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao, Kun Zhang
    International Conference on Artificial Intelligence and Statistics, 2024
    PDF
  6. ICLR Oral
    Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
    Haoyue Dai, Ignavier Ng, Gongxu Luo, Petar Stojanov, Peter Spirtes, Kun Zhang
    International Conference on Learning Representations, 2024
    PDF
  7. ICLR
    A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
    Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang
    International Conference on Learning Representations, 2024
    PDF
  8. ICLR
    Federated Causal Discovery from Heterogeneous Data
    Longkang Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang
    International Conference on Learning Representations, 2024
    PDF

2023

  1. NeurIPS
    On the Identifiability of Sparse ICA without Assuming Non-Gaussianity
    Ignavier Ng, Yujia Zheng, Xinshuai Dong, Kun Zhang
    Advances in Neural Information Processing Systems, 2023
    PDF
  2. ICLR
    Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks
    Yujia Zheng, Ignavier Ng, Yewen Fan, Kun Zhang
    International Conference on Learning Representations, 2023
    PDF

2022

  1. NeurIPS
    Truncated Matrix Power Iteration for Differentiable DAG Learning
    Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Ehsan M Abbasnejad, Mingming Gong, Kun Zhang, Javen Qinfeng Shi
    Advances in Neural Information Processing Systems, 2022
    PDF Code
  2. NeurIPS
    MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models
    Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard Bondell
    Advances in Neural Information Processing Systems, 2022
    PDF Code
  3. NeurIPS
    On the Identifiability of Nonlinear ICA: Sparsity and Beyond
    Yujia Zheng, Ignavier Ng, Kun Zhang
    Advances in Neural Information Processing Systems, 2022
    ICLR Workshop on Objects, Structure, and Causality,     2022 (Oral)
    PDF
  4. AISTATS
    On the Convergence of Continuous Constrained Optimization for Structure Learning
    Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien, Kun Zhang
    International Conference on Artificial Intelligence and Statistics, 2022
    NeurIPS Workshop on Causal Discovery and Causality-Inspired Machine Learning,     2020 (Oral)
    PDF Code
  5. AISTATS
    Towards Federated Bayesian Network Structure Learning with Continuous Optimization
    Ignavier Ng, Kun Zhang
    International Conference on Artificial Intelligence and Statistics, 2022
    PDF Code
  6. SDM
    Masked Gradient-Based Causal Structure Learning
    Ignavier Ng, Shengyu Zhu, Zhuangyan Fang, Haoyang Li, Zhitang Chen, Jun Wang
    SIAM International Conference on Data Mining, 2022
    PDF Code
  7. IJGIS
    STICC: a multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity
    Yuhao Kang, Kunlin Wu, Song Gao, Ignavier Ng, Jinmeng Rao, Shan Ye, Fan Zhang, Teng Fei
    International Journal of Geographical Information Science, 2022
    PDF Code

2021

  1. NeurIPS
    Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions
    Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang
    Advances in Neural Information Processing Systems, 2021
    PDF Code

2020

  1. NeurIPS
    On the Role of Sparsity and DAG Constraints for Learning Linear DAGs
    Ignavier Ng, AmirEmad Ghassami, Kun Zhang
    Advances in Neural Information Processing Systems, 2020
    PDF Code
  2. ICLR Oral
    Causal Discovery with Reinforcement Learning
    Shengyu Zhu, Ignavier Ng, Zhitang Chen
    International Conference on Learning Representations, 2020
    PDF Code