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Today's Events

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

    HEC: 101A: 101A and Virtual

    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 …

    College of Engineering and Computer Science