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Differentiable Structure Learning for General Binary Data
NeurIPS 2025 -
Markov Equivalence and Consistency in Differentiable Structure Learning
NeurIPS 2024 -
Data-driven Forecasting and Reference Prices with Exposure Effect
Naval Research Logistics 2024 -
High-dimensional sparse single–index regression via Hilbert–Schmidt independence criterion
Statistics and Computing 2024 -
Global Optimality in Bivariate Gradient-based DAG Learning
NeurIPS 2023 -
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 fourth-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 causal discovery, mainly learning structure from data, with a particular emphasis on learning causal structure. Additionally, I maintain a keen interest in optimization techniques within the realm of machine learning.
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