55 citations · 95 across the 19 of their papers we have counts for
4 papers · 1 filter
Differentiable Causal Discovery For Latent Hierarchical Causal Models
Parjanya Prashant, Ignavier Ng, Kun Zhang +1
Discovering causal structures with latent variables from observational data is a fundamental challenge in causal discovery. Existing methods often rely on constraint-based, iterati…
Revisiting Differentiable Structure Learning: Inconsistency of Penalty and Beyond
Kaifeng Jin, Ignavier Ng, Kun Zhang +1
Recent advances in differentiable structure learning have framed the combinatorial problem of learning directed acyclic graphs as a continuous optimization problem. Various aspects…
A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery
Yingyu Lin, Yuxing Huang, Wenqin Liu +6
Real-world data often violates the equal-variance assumption (homoscedasticity), making it essential to account for heteroscedastic noise in causal discovery. In this work, we expl…
On the Identifiability of Sparse ICA without Assuming Non-Gaussianity
Ignavier Ng, Yujia Zheng, Xinshuai Dong +1
Independent component analysis (ICA) is a fundamental statistical tool used to reveal hidden generative processes from observed data. However, traditional ICA approaches struggle w…