College of Engineering and Computer Science: by Category “Speaker/Lecture/Seminar”

Causal Machine Learning: Continuous structure learning and identifiability of causal invariances

Speaker: Dr. Kevin Bello From: Soroco Abstract Interpretability and causality are key desiderata in modern machine learning systems. Graphical models, and more specifically directed acyclic graphs (DAGs, a.k.a. Bayesian networks), serve as a well-established tool for expressing interpretable causal relationships. However, the task of estimating DAG structures from data poses a significant challenge, given its inherently complex combinatorial nature, and …