Publications

2026

1. Score-based Greedy Search for Structure Identification of Partially Observed Causal Models

   Xinshuai Dong, Ignavier Ng, Haoyue Dai, Jiaqi Sun, Xiangchen Song, Peter Spirtes, Kun Zhang

   International Conference on Learning Representations (ICLR), 2026

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2. Revisiting Differentiable Structure Learning: Inconsistency of L1 Penalty and Beyond

   Kaifeng Jin, Ignavier Ng, Kun Zhang, Biwei Huang

   AAAI Conference on Artificial Intelligence (AAAI), 2026

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2025

1. 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 (ICML), 2025

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2. 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 (ICML), 2025

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3. Latent Variable Causal Discovery under Selection Bias

   Haoyue Dai, Yiwen Qiu, Ignavier Ng, Xinshuai Dong, Peter Spirtes, Kun Zhang

   International Conference on Machine Learning (ICML), 2025

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4. 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 (AISTATS), 2025

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5. 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 (ICLR), 2025 (Oral)

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6. 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 (ICLR), 2025

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7. Differentiable Causal Discovery for Latent Hierarchical Causal Models

   Parjanya Prajakta Prashant, Ignavier Ng, Kun Zhang, Biwei Huang

   International Conference on Learning Representations (ICLR), 2025

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8. 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 (ICLR), 2025

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9. 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 (ICLR), 2025

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2024

1. 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 (NeurIPS), 2024

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2. 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 (ICML), 2024

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3. Causal Representation Learning from Multiple Distributions: A General Setting

   Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng

   International Conference on Machine Learning (ICML), 2024

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4. Structure Learning with Continuous Optimization: A Sober Look and Beyond

   Ignavier Ng, Biwei Huang, Kun Zhang

   Conference on Causal Learning and Reasoning (CLeaR), 2024 (Best Paper Award)

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5. 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 (AISTATS), 2024

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6. 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 (ICLR), 2024 (Oral)

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7. 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 (ICLR), 2024

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8. Federated Causal Discovery from Heterogeneous Data

   Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang

   International Conference on Learning Representations (ICLR), 2024

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2023

1. On the Identifiability of Sparse ICA without Assuming Non-Gaussianity

   Ignavier Ng, Yujia Zheng, Xinshuai Dong, Kun Zhang

   Advances in Neural Information Processing Systems (NeurIPS), 2023

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2. Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks

   Yujia Zheng, Ignavier Ng, Yewen Fan, Kun Zhang

   International Conference on Learning Representations (ICLR), 2023

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2022

1. 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 (NeurIPS), 2022

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2. 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 (NeurIPS), 2022

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3. On the Identifiability of Nonlinear ICA: Sparsity and Beyond

   Yujia Zheng, Ignavier Ng, Kun Zhang

   Advances in Neural Information Processing Systems (NeurIPS), 2022

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4. 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 (AISTATS), 2022
   NeurIPS Workshop on Causal Discovery and Causality-Inspired Machine Learning, 2020 (Oral)

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5. Towards Federated Bayesian Network Structure Learning with Continuous Optimization

   Ignavier Ng, Kun Zhang

   International Conference on Artificial Intelligence and Statistics (AISTATS), 2022

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6. Masked Gradient-Based Causal Structure Learning

   Ignavier Ng, Shengyu Zhu, Zhuangyan Fang, Haoyang Li, Zhitang Chen, Jun Wang

   SIAM International Conference on Data Mining (SDM), 2022

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7. 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 (IJGIS), 2022

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2021

1. Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions

   Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang

   Advances in Neural Information Processing Systems (NeurIPS), 2021

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2020

1. On the Role of Sparsity and DAG Constraints for Learning Linear DAGs

   Ignavier Ng, AmirEmad Ghassami, Kun Zhang

   Advances in Neural Information Processing Systems (NeurIPS), 2020

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2. Causal Discovery with Reinforcement Learning

   Shengyu Zhu, Ignavier Ng, Zhitang Chen

   International Conference on Learning Representations (ICLR), 2020 (Oral)

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