Ignavier Ng
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
21
Venues
5
Active years
2020–2026
Best venue rank
A*
Where they publish
Papers
21 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | AAAI | Revisiting Differentiable Structure Learning: Inconsistency of L1 Penalty and Beyond. | Kaifeng Jin, Ignavier Ng, Kun Zhang, Biwei Huang |
| 2025 | AISTATS | Causal Representation Learning from General Environments under Nonparametric Mixing. | Ignavier Ng, Shaoan Xie, Xinshuai Dong, Peter Spirtes, Kun Zhang |
| 2025 | 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 |
| 2025 | 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 |
| 2025 | ICLR | 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 |
| 2025 | 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 |
| 2025 | ICLR | Differentiable Causal Discovery for Latent Hierarchical Causal Models. | Parjanya Prajakta Prashant, Ignavier Ng, Kun Zhang, Biwei Huang |
| 2025 | ICML | Latent Variable Causal Discovery under Selection Bias. | Haoyue Dai, Yiwen Qiu, Ignavier Ng, Xinshuai Dong, Peter Spirtes, Kun Zhang |
| 2025 | 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 |
| 2025 | ICML | A General Representation-Based Approach to Multi-Source Domain Adaptation. | Ignavier Ng, Yan Li, Zijian Li, Yujia Zheng, Guangyi Chen, Kun Zhang |
| 2024 | AISTATS | Local Causal Discovery with Linear non-Gaussian Cyclic Models. | Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao, Kun Zhang |
| 2024 | ICLR | Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View. | Haoyue Dai, Ignavier Ng, Gongxu Luo, Peter Spirtes, Petar Stojanov, Kun Zhang |
| 2024 | 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 |
| 2024 | ICLR | Federated Causal Discovery from Heterogeneous Data. | Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang |
| 2024 | ICML | Causal Representation Learning from Multiple Distributions: A General Setting. | Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng |
| 2024 | ICML | Score-Based Causal Discovery of Latent Variable Causal Models. | Ignavier Ng, Xinshuai Dong, Haoyue Dai, Biwei Huang, Peter Spirtes, Kun Zhang |
| 2023 | ICLR | Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks. | Yujia Zheng, Ignavier Ng, Yewen Fan, Kun Zhang |
| 2022 | AISTATS | Towards Federated Bayesian Network Structure Learning with Continuous Optimization. | Ignavier Ng, Kun Zhang |
| 2022 | AISTATS | On the Convergence of Continuous Constrained Optimization for Structure Learning. | Ignavier Ng, Sbastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien, Kun Zhang |
| 2022 | SDM | Masked Gradient-Based Causal Structure Learning. | Ignavier Ng, Shengyu Zhu, Zhuangyan Fang, Haoyang Li, Zhitang Chen, Jun Wang |
| 2020 | ICLR | Causal Discovery with Reinforcement Learning. | Shengyu Zhu, Ignavier Ng, Zhitang Chen |