Ignavier Ng
28 papers · 2020–2026 · 6 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (32) π Conference Polyglot Β· Silver π Academic Marathon Β· Sprint π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
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Deep Specialist
(18)
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Keyword Champion
(2)
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Grand Slam
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Dynamic Duo
(27)
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Triple Crown
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Prolific Year Β· Productive
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Unstoppable Β· Bronze
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Century Club Β· Bronze
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Renaissance Researcher
(5)
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Keyword Collector
(113)
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Research topics
Machine Learning
(23)
Artificial Intelligence
(20)
Core AI
(19)
Knowledge & Reasoning
(10)
Reasoning
(10)
Learning Types
(9)
Optimization & Theory
(9)
Core Methods
(8)
Mathematics & Optimization
(7)
Optimization
(6)
Bayesian & Probabilistic Β· Machine Learning
(4)
Mathematics
(4)
Deep Learning
(2)
Application Areas
(2)
Bayesian & Probabilistic Β· Artificial Intelligence
(2)
Keywords
causal discovery
(13)
directed acyclic graph
(9)
structure learning
(7)
continuous optimization
(5)
latent variable
(4)
conditional independence
(3)
graphical model
(3)
markov equivalence class
(2)
selection bia
(2)
additive noise model
(2)
causal representation learning
(2)
sparsity constraint
(2)
source separation
(2)
markov blanket
(2)
score function
(2)
bayesian network
(2)
latent variable model
(2)
independent component analysis
(2)
causal structure
(2)
latent causal variable
(2)
Papers
Revisiting Differentiable Structure Learning: Inconsistency of L1 Penalty and Beyond
AAAI 2026
A General Representation-Based Approach to Multi-Source Domain Adaptation
ICML 2025
Analytic DAG Constraints for Differentiable DAG Learning
ICLR 2025
A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery
ICLR 2025
Causal Representation Learning from General Environments under Nonparametric Mixing
AISTATS 2025
Differentiable Causal Discovery for Latent Hierarchical Causal Models
ICLR 2025
Latent Variable Causal Discovery under Selection Bias
ICML 2025
Permutation-based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data
ICML 2025
Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning
ICLR 2025
When Selection Meets Intervention: Additional Complexities in Causal Discovery
ICLR 2025
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
ICLR 2024
Causal Representation Learning from Multiple Distributions: A General Setting
ICML 2024
Federated Causal Discovery from Heterogeneous Data
ICLR 2024
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
ICLR 2024
Local Causal Discovery with Linear non-Gaussian Cyclic Models
AISTATS 2024
On the Parameter Identifiability of Partially Observed Linear Causal Models
NeurIPS 2024
Score-Based Causal Discovery of Latent Variable Causal Models
ICML 2024
Structure Learning with Continuous Optimization: A Sober Look and Beyond
CLeaR 2024
Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks
ICLR 2023
On the Identifiability of Sparse ICA without Assuming Non-Gaussianity
NeurIPS 2023
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models
NeurIPS 2022
On the Convergence of Continuous Constrained Optimization for Structure Learning
AISTATS 2022
On the Identifiability of Nonlinear ICA: Sparsity and Beyond
NeurIPS 2022
Towards Federated Bayesian Network Structure Learning with Continuous Optimization
AISTATS 2022
Truncated Matrix Power Iteration for Differentiable DAG Learning
NeurIPS 2022
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions
NeurIPS 2021
Causal Discovery with Reinforcement Learning
ICLR 2020
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs
NeurIPS 2020