Ignavier Ng

According to our database, Ignavier Ng authored at least 34 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

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

CoRR, January, 2025

Identification of Nonparametric Dynamic Causal Structure and Latent Process in Climate System.

Minghao Fu, Biwei Huang, Zijian Li, Yujia Zheng, Ignavier Ng, Yingyao Hu, Kun Zhang

CoRR, January, 2025

Differentiable Causal Discovery for Latent Hierarchical Causal Models.

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

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

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

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

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

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

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

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

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

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Causal Representation Learning from General Environments under Nonparametric Mixing.

Ignavier Ng, Shaoan Xie, Xinshuai Dong, Peter Spirtes, Kun Zhang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Revisiting Differentiable Structure Learning: Inconsistency of ℓ₁ Penalty and Beyond.

Kaifeng Jin, Ignavier Ng, Kun Zhang, Biwei Huang

CoRR, 2024

Continual Learning of Nonlinear Independent Representations.

Boyang Sun, Ignavier Ng, Guangyi Chen, Yifan Shen, Qirong Ho, Kun Zhang

CoRR, 2024

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

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Score-Based Causal Discovery of Latent Variable Causal Models.

Ignavier Ng, Xinshuai Dong, Haoyue Dai, Biwei Huang, Peter Spirtes, Kun Zhang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Causal Representation Learning from Multiple Distributions: A General Setting.

Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Federated Causal Discovery from Heterogeneous Data.

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View.

Haoyue Dai, Ignavier Ng, Gongxu Luo, Peter Spirtes, Petar Stojanov, Kun Zhang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

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

Ignavier Ng, Biwei Huang, Kun Zhang

Proceedings of the Causal Learning and Reasoning, 2024

Local Causal Discovery with Linear non-Gaussian Cyclic Models.

Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao, Kun Zhang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

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

Ignavier Ng, Yujia Zheng, Xinshuai Dong, Kun Zhang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks.

Yujia Zheng, Ignavier Ng, Yewen Fan, Kun Zhang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

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

Int. J. Geogr. Inf. Sci., 2022

Masked Gradient-Based Causal Structure Learning.

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

Proceedings of the 2022 SIAM International Conference on Data Mining, 2022

On the Identifiability of Nonlinear ICA: Sparsity and Beyond.

Yujia Zheng, Ignavier Ng, Kun Zhang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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 D. Bondell

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Truncated Matrix Power Iteration for Differentiable DAG Learning.

Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Ehsan Abbasnejad, Mingming Gong, Kun Zhang, Javen Qinfeng Shi

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On the Convergence of Continuous Constrained Optimization for Structure Learning.

Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien, Kun Zhang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Towards Federated Bayesian Network Structure Learning with Continuous Optimization.

Ignavier Ng, Kun Zhang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

gCastle: A Python Toolbox for Causal Discovery.

Keli Zhang, Shengyu Zhu, Marcus Kalander, Ignavier Ng, Junjian Ye, Zhitang Chen, Lujia Pan

CoRR, 2021

Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions.

Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

On the Convergence of Continuous Constrained Optimization for Structure Learning.

Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien

CoRR, 2020

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

Ignavier Ng, AmirEmad Ghassami, Kun Zhang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Causal Discovery with Reinforcement Learning.

Shengyu Zhu, Ignavier Ng, Zhitang Chen

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

A Graph Autoencoder Approach to Causal Structure Learning.

Ignavier Ng, Shengyu Zhu, Zhitang Chen, Zhuangyan Fang

CoRR, 2019

Masked Gradient-Based Causal Structure Learning.

Ignavier Ng, Zhuangyan Fang, Shengyu Zhu, Zhitang Chen

CoRR, 2019