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Ignavier Ng
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2020 – today
- 2024
- [c18]Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao, Kun Zhang:
Local Causal Discovery with Linear non-Gaussian Cyclic Models. AISTATS 2024: 154-162 - [c17]Ignavier Ng, Biwei Huang, Kun Zhang:
Structure Learning with Continuous Optimization: A Sober Look and Beyond. CLeaR 2024: 71-105 - [c16]Haoyue Dai, Ignavier Ng, Gongxu Luo, Peter Spirtes, Petar Stojanov, Kun Zhang:
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View. ICLR 2024 - [c15]Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang:
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables. ICLR 2024 - [c14]Longkang Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang:
Federated Causal Discovery from Heterogeneous Data. ICLR 2024 - [c13]Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng:
Causal Representation Learning from Multiple Distributions: A General Setting. ICML 2024 - [c12]Ignavier Ng, Xinshuai Dong, Haoyue Dai, Biwei Huang, Peter Spirtes, Kun Zhang:
Score-Based Causal Discovery of Latent Variable Causal Models. ICML 2024 - [i21]Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng:
Causal Representation Learning from Multiple Distributions: A General Setting. CoRR abs/2402.05052 (2024) - [i20]Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen
, Tongliang Liu, Bin Gu, Kun Zhang:
Federated Causal Discovery from Heterogeneous Data. CoRR abs/2402.13241 (2024) - [i19]Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao, Kun Zhang:
Local Causal Discovery with Linear non-Gaussian Cyclic Models. CoRR abs/2403.14843 (2024) - [i18]Haoyue Dai, Ignavier Ng, Gongxu Luo, Peter Spirtes, Petar Stojanov, Kun Zhang:
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View. CoRR abs/2403.15500 (2024) - [i17]Xinshuai Dong, Ignavier Ng, Biwei Huang, Yuewen Sun, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang:
On the Parameter Identifiability of Partially Observed Linear Causal Models. CoRR abs/2407.16975 (2024) - [i16]Boyang Sun, Ignavier Ng, Guangyi Chen, Yifan Shen, Qirong Ho, Kun Zhang:
Continual Learning of Nonlinear Independent Representations. CoRR abs/2408.05788 (2024) - [i15]Ignavier Ng, Yujia Zheng, Xinshuai Dong, Kun Zhang:
On the Identifiability of Sparse ICA without Assuming Non-Gaussianity. CoRR abs/2408.10353 (2024) - 2023
- [c11]Yujia Zheng, Ignavier Ng, Yewen Fan, Kun Zhang:
Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks. ICLR 2023 - [c10]Ignavier Ng, Yujia Zheng, Xinshuai Dong, Kun Zhang:
On the Identifiability of Sparse ICA without Assuming Non-Gaussianity. NeurIPS 2023 - [i14]Ignavier Ng, Biwei Huang, Kun Zhang:
Structure Learning with Continuous Optimization: A Sober Look and Beyond. CoRR abs/2304.02146 (2023) - [i13]Yujia Zheng, Ignavier Ng, Yewen Fan, Kun Zhang:
Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks. CoRR abs/2305.11379 (2023) - [i12]Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang:
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables. CoRR abs/2312.11001 (2023) - 2022
- [j1]Yuhao Kang
STICC: a multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity. Int. J. Geogr. Inf. Sci. 36(8): 1518-1549 (2022) - [c9]Ignavier Ng, Kun Zhang:
Towards Federated Bayesian Network Structure Learning with Continuous Optimization. AISTATS 2022: 8095-8111 - [c8]Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien, Kun Zhang:
On the Convergence of Continuous Constrained Optimization for Structure Learning. AISTATS 2022: 8176-8198 - [c7]Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Ehsan Abbasnejad, Mingming Gong, Kun Zhang, Javen Qinfeng Shi:
Truncated Matrix Power Iteration for Differentiable DAG Learning. NeurIPS 2022 - [c6]Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard D. Bondell:
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. NeurIPS 2022 - [c5]Yujia Zheng, Ignavier Ng, Kun Zhang:
On the Identifiability of Nonlinear ICA: Sparsity and Beyond. NeurIPS 2022 - [c4]Ignavier Ng, Shengyu Zhu, Zhuangyan Fang, Haoyang Li, Zhitang Chen, Jun Wang:
Masked Gradient-Based Causal Structure Learning. SDM 2022: 424-432 - [i11]Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang:
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions. CoRR abs/2201.05666 (2022) - [i10]Yuhao Kang, Kunlin Wu, Song Gao, Ignavier Ng, Jinmeng Rao, Shan Ye
STICC: A multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity. CoRR abs/2203.09611 (2022) - [i9]Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. CoRR abs/2205.13869 (2022) - [i8]Yujia Zheng, Ignavier Ng, Kun Zhang:
On the Identifiability of Nonlinear ICA: Sparsity and Beyond. CoRR abs/2206.07751 (2022) - [i7]Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, M. Ehsan Abbasnejad, Mingming Gong, Kun Zhang, Javen Qinfeng Shi
Truncated Matrix Power Iteration for Differentiable DAG Learning. CoRR abs/2208.14571 (2022) - 2021
- [c3]Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang:
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions. NeurIPS 2021: 20308-20320 - [i6]Ignavier Ng, Kun Zhang:
Towards Federated Bayesian Network Structure Learning with Continuous Optimization. CoRR abs/2110.09356 (2021) - [i5]Keli Zhang, Shengyu Zhu, Marcus Kalander, Ignavier Ng, Junjian Ye, Zhitang Chen, Lujia Pan:
gCastle: A Python Toolbox for Causal Discovery. CoRR abs/2111.15155 (2021) - 2020
- [c2]Shengyu Zhu, Ignavier Ng, Zhitang Chen:
Causal Discovery with Reinforcement Learning. ICLR 2020 - [c1]Ignavier Ng, AmirEmad Ghassami, Kun Zhang:
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs. NeurIPS 2020 - [i4]Ignavier Ng, AmirEmad Ghassami, Kun Zhang:
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs. CoRR abs/2006.10201 (2020) - [i3]Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien:
On the Convergence of Continuous Constrained Optimization for Structure Learning. CoRR abs/2011.11150 (2020)
2010 – 2019
- 2019
- [i2]Ignavier Ng, Zhuangyan Fang, Shengyu Zhu, Zhitang Chen:
Masked Gradient-Based Causal Structure Learning. CoRR abs/1910.08527 (2019) - [i1]Ignavier Ng, Shengyu Zhu, Zhitang Chen, Zhuangyan Fang:
A Graph Autoencoder Approach to Causal Structure Learning. CoRR abs/1911.07420 (2019)
Coauthor Index
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last updated on 2024-10-07 21:15 CEST by the dblp team
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