← Authors

Ignavier Ng

28 papers · 2020–2026 · 6 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (5) πŸƒ Academic Marathon (5) 🐝 Cross-Pollinator (15) πŸ—ΊοΈ Taxonomy Completionist (19) πŸ‘‘ Triple Crown 🀝 Dynamic Duo (26) πŸ† Grand Slam πŸ’Ž Century Club (27) πŸ”₯ Unstoppable (6) ⚑ Prolific Year (5) πŸ—ƒοΈ Keyword Collector (53)

Conferences

ICLR (10) NIPS (7) ICML (5) AISTATS (4) AAAI (1) CLEAR (1)

Top co-authors

Kun Zhang (27) Yujia Zheng (9) Xinshuai Dong (9) Biwei Huang (9) Peter Spirtes (8) Haoyue Dai (6) Mingming Gong (4) Gongxu Luo (3) Shaoan Xie (3) Guangyi Chen (3)

Keywords

causal discovery (6) directed acyclic graph (5) continuous optimization (5) structure learning (4) source separation (2) graphical model (2) independent component analysis (2) bayesian network (2) second-order statistics (1) causal structure learning (1) nonconvex optimization (1) privacy preservation (1) latent variable model (1) causal structure (1) expectation maximization (1) parameter identifiability (1) constraint optimization (1) nonlinear independent component analysis (1) sparse structure learning (1) sparse representation (1)

Papers

Revisiting Differentiable Structure Learning: Inconsistency of L1 Penalty and Beyond AAAI 2026
Permutation-based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data ICML 2025
Analytic DAG Constraints for Differentiable DAG Learning ICLR 2025
A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery ICLR 2025
Latent Variable Causal Discovery under Selection Bias ICML 2025
When Selection Meets Intervention: Additional Complexities in Causal Discovery ICLR 2025
Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning ICLR 2025
Differentiable Causal Discovery for Latent Hierarchical Causal Models ICLR 2025
Causal Representation Learning from General Environments under Nonparametric Mixing AISTATS 2025
A General Representation-Based Approach to Multi-Source Domain Adaptation ICML 2025
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables ICLR 2024
On the Parameter Identifiability of Partially Observed Linear Causal Models NIPS 2024
Local Causal Discovery with Linear non-Gaussian Cyclic Models AISTATS 2024
Structure Learning with Continuous Optimization: A Sober Look and Beyond CLEAR 2024
Federated Causal Discovery from Heterogeneous Data ICLR 2024
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View ICLR 2024
Score-Based Causal Discovery of Latent Variable Causal Models ICML 2024
Causal Representation Learning from Multiple Distributions: A General Setting ICML 2024
On the Identifiability of Sparse ICA without Assuming Non-Gaussianity NIPS 2023
Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks ICLR 2023
Towards Federated Bayesian Network Structure Learning with Continuous Optimization AISTATS 2022
Truncated Matrix Power Iteration for Differentiable DAG Learning NIPS 2022
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models NIPS 2022
On the Identifiability of Nonlinear ICA: Sparsity and Beyond NIPS 2022
On the Convergence of Continuous Constrained Optimization for Structure Learning AISTATS 2022
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions NIPS 2021
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs NIPS 2020
Causal Discovery with Reinforcement Learning ICLR 2020