Masked Gradient-Based Causal Structure Learning. - Ignavier Ng; Zhuangyan Fang; Shengyu Zhu 001; Zhitang Chen (CoRR, 2019)

A Graph Autoencoder Approach to Causal Structure Learning. - Ignavier Ng; Shengyu Zhu 001; Zhitang Chen; Zhuangyan Fang (CoRR, 2019)

Causal Discovery with Reinforcement Learning. - Shengyu Zhu 001; Ignavier Ng; Zhitang Chen (ICLR, 2020)

On the Role of Sparsity and DAG Constraints for Learning Linear DAGs. - Ignavier Ng; AmirEmad Ghassami; Kun Zhang 001 (NeurIPS, 2020)

On the Convergence of Continuous Constrained Optimization for Structure Learning. - Ignavier Ng; Sébastien Lachapelle; Nan Rosemary Ke; Simon Lacoste-Julien (CoRR, 2020)

Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions. - Ignavier Ng; Yujia Zheng 001; Jiji Zhang; Kun Zhang 001 (NeurIPS, 2021)

gCastle: A Python Toolbox for Causal Discovery. - Keli Zhang; Shengyu Zhu 001; Marcus Kalander; Ignavier Ng; Junjian Ye; Zhitang Chen; Lujia Pan (CoRR, 2021)

Towards Federated Bayesian Network Structure Learning with Continuous Optimization. - Ignavier Ng; Kun Zhang 001 (AISTATS, 2022)

On the Convergence of Continuous Constrained Optimization for Structure Learning. - Ignavier Ng; Sébastien Lachapelle; Nan Rosemary Ke; Simon Lacoste-Julien; Kun Zhang 001 (AISTATS, 2022)

Truncated Matrix Power Iteration for Differentiable DAG Learning. - Zhen Zhang 008; Ignavier Ng; Dong Gong; Yuhang Liu 002; Ehsan Abbasnejad; Mingming Gong; Kun Zhang 001; Javen Qinfeng Shi (NeurIPS, 2022)

MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. - Erdun Gao; Ignavier Ng; Mingming Gong; Li Shen 008; Wei Huang; Tongliang Liu; Kun Zhang 001; Howard D. Bondell (NeurIPS, 2022)

On the Identifiability of Nonlinear ICA: Sparsity and Beyond. - Yujia Zheng 001; Ignavier Ng; Kun Zhang 001 (NeurIPS, 2022)

Masked Gradient-Based Causal Structure Learning. - Ignavier Ng; Shengyu Zhu 001; Zhuangyan Fang; Haoyang Li 002; Zhitang Chen; Jun Wang 012 (SDM, 2022)

STICC: a multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity. - Yuhao Kang; Kunlin Wu; Song Gao 001; Ignavier Ng; Jinmeng Rao; Shan Ye; Fan Zhang 011; Teng Fei 001 (Int. J. Geogr. Inf. Sci., 2022)

Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks. - Yujia Zheng 001; Ignavier Ng; Yewen Fan; Kun Zhang 001 (ICLR, 2023)

On the Identifiability of Sparse ICA without Assuming Non-Gaussianity. - Ignavier Ng; Yujia Zheng 001; Xinshuai Dong; Kun Zhang 001 (NeurIPS, 2023)

Local Causal Discovery with Linear non-Gaussian Cyclic Models. - Haoyue Dai; Ignavier Ng; Yujia Zheng 001; Zhengqing Gao; Kun Zhang 001 (AISTATS, 2024)

Structure Learning with Continuous Optimization: A Sober Look and Beyond. - Ignavier Ng; Biwei Huang; Kun Zhang 001 (CLeaR, 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 001 (ICLR, 2024)

A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables. - Xinshuai Dong; Biwei Huang; Ignavier Ng; Xiangchen Song; Yujia Zheng 001; Songyao Jin; Roberto Legaspi; Peter Spirtes; Kun Zhang 001 (ICLR, 2024)

Federated Causal Discovery from Heterogeneous Data. - Loka Li; Ignavier Ng; Gongxu Luo; Biwei Huang; Guangyi Chen 002; Tongliang Liu; Bin Gu 001; Kun Zhang 001 (ICLR, 2024)

Causal Representation Learning from Multiple Distributions: A General Setting. - Kun Zhang 001; Shaoan Xie; Ignavier Ng; Yujia Zheng 001 (ICML, 2024)

Score-Based Causal Discovery of Latent Variable Causal Models. - Ignavier Ng; Xinshuai Dong; Haoyue Dai; Biwei Huang; Peter Spirtes; Kun Zhang 001 (ICML, 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 001 (NeurIPS, 2024)

Continual Learning of Nonlinear Independent Representations. - Boyang Sun; Ignavier Ng; Guangyi Chen 002; Yifan Shen 004; Qirong Ho; Kun Zhang 001 (CoRR, 2024)

Revisiting Differentiable Structure Learning: Inconsistency of ℓ1 Penalty and Beyond. - Kaifeng Jin; Ignavier Ng; Kun Zhang 001; Biwei Huang (CoRR, 2024)

Causal Representation Learning from General Environments under Nonparametric Mixing. - Ignavier Ng; Shaoan Xie; Xinshuai Dong; Peter Spirtes; Kun Zhang 001 (AISTATS, 2025)

Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning. - Zijian Li 001; Shunxing Fan; Yujia Zheng 001; Ignavier Ng; Shaoan Xie; Guangyi Chen 002; Xinshuai Dong; Ruichu Cai; Kun Zhang 001 (ICLR, 2025)

Analytic DAG Constraints for Differentiable DAG Learning. - Zhen Zhang 008; Ignavier Ng; Dong Gong; Yuhang Liu 002; Mingming Gong; Biwei Huang; Kun Zhang 001; Anton van den Hengel; Javen Qinfeng Shi (ICLR, 2025)

When Selection Meets Intervention: Additional Complexities in Causal Discovery. - Haoyue Dai; Ignavier Ng; Jianle Sun; Zeyu Tang 002; Gongxu Luo; Xinshuai Dong; Peter Spirtes; Kun Zhang 001 (ICLR, 2025)

A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery. - Yingyu Lin; Yuxing Huang; Wenqin Liu; Haoran Deng; Ignavier Ng; Kun Zhang 001; Mingming Gong; Yian Ma; Biwei Huang (ICLR, 2025)

Differentiable Causal Discovery for Latent Hierarchical Causal Models. - Parjanya Prajakta Prashant; Ignavier Ng; Kun Zhang 001; Biwei Huang (ICLR, 2025)

Identification of Nonparametric Dynamic Causal Structure and Latent Process in Climate System. - Minghao Fu 002; Biwei Huang; Zijian Li 001; Yujia Zheng 001; Ignavier Ng; Yingyao Hu; Kun Zhang 001 (CoRR, 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 001 (CoRR, 2025)