|Other - CCTAILS - CCT's AI Lecture Series|
|Continuous Optimization for Learning Bayesian Networks|
|Dr. Yue Yu, Lehigh University|
|Associate Professor of Applied Mathematics|
|Zoom/Digital Media Center for Viewing Zoom/Theatre
February 08, 2023 - 03:00 pm
Note: This lecture will be presented via zoom and available for viewing in the Digital Media Center Theatre.
Bayesian networks are directed probabilistic graphical models used to compactly model joint probability distributions of data. Automatic discovery of their directed acyclic graph (DAG) structure is important to causal inference tasks. However, learning a DAG from observed samples of an unknown joint distribution is generally a challenging combinatorial problem, owing to the intractable search space superexponential in the number of graph nodes. A recent breakthrough formulates the problem as a continuous optimization with a structural constraint that ensures acyclicity (NOTEARS, Zheng et al., 2018), which enables a suite of continuous optimization techniques to be used and employs an augmented Lagrangian method to apply the constraint.
Yue Yu received her B.S. from Peking University in 2008, and her Ph.D. from Brown University in 2014. She was a postdoc fellow at Harvard University after graduation, and then she joined Lehigh University as an assistant professor of applied mathematics and was promoted to associate professor in 2019. Her research lies in the area of applied and computational mathematics, with recent projects focusing on nonlocal problems and scientific machine learning. She has received an NSF Early Career award and an AFOSR Young Investigator Program (YIP) award.