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Global Optimality in Bivariate Gradient-based DAG Learning
NeurIPS 2023
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Optimizing NOTEARS objective via topological swaps
ICML 2023 | Code | Proceedings -
A simple approach to balance task loss in multi-task learning
IEEE Big Data 2021 -
Deep multi-task augmented feature learning via hierarchical graph neural network
ECML PKDD 2021
Chang Deng
Hi there 👋!
I am a second-year PhD student in Econometrics and Statistics at the University of Chicago (UChicago) Booth School of Business. Before joining Booth, I earned a Master’s degree in Computational and Applied Mathematics at UChicago, advised by Bryon Aragam. Prior to coming to UChicago, I obtained my Bachelor’s degree in Applied Mathematics at the Southern University of Science and Technology (SUSTech), where I was fortunate to work with Bingsheng He.
My research primarily centers on learning structure from data, with a particular emphasis on learning Directed Acyclic Graphs (DAGs) from data. Additionally, I maintain a keen interest in optimization techniques within the realm of machine learning.
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