216 citations · 454 across the 55 of their papers we have counts for
Showing 2023 · stat.MLShow all
3 papers · 2 filters
stat.ML2023★ 1 cited
Learning nonparametric latent causal graphs with unknown interventions
Yibo Jiang, Bryon Aragam
We establish conditions under which latent causal graphs are nonparametrically identifiable and can be reconstructed from unknown interventions in the latent space. Our primary foc…
stat.ML2023★ 1 cited
Optimizing NOTEARS Objectives via Topological Swaps
Chang Deng, Kevin Bello, Bryon Aragam +1
Recently, an intriguing class of non-convex optimization problems has emerged in the context of learning directed acyclic graphs (DAGs). These problems involve minimizing a given l…
stat.ML2023★ 1 cited
Neuro-Causal Factor Analysis
Alex Markham, Mingyu Liu, Bryon Aragam +1
Factor analysis (FA) is a statistical method for explaining how mutually dependent observed variables can be represented in terms of mutually independent latent factors, and it is…