* Equal contribution.

1. Nonasymptotic causal structure learning in generalized linear DAGs with continuous optimization. C Deng^*, I Lee^*, B Aragam. (Preprint 2026)

2. Differentiable structure learning and causal discovery for general binary data. C Deng, B Aragam. (NeurIPS 2025)

3. Markov equivalence and consistency in differentiable structure learning. C Deng, K Bello, P Ravikumar, B Aragam. (NeurIPS 2024)

4. Data-driven forecasting and reference prices with exposure effect. O Baron, C Deng, S He, H Yuan. (Naval Research Logistics 2024)

5. High-dimensional sparse single–index regression via Hilbert–Schmidt independence criterion. X Chen, C Deng, S He, R Wu, J Zhang. (Statistics and Computing 2024)

6. Global optimality in bivariate gradient-based DAG learning. C Deng, K Bello, B Aragam, P Ravikumar. (NeurIPS 2023)

7. Optimizing NOTEARS objective via topological swaps. C Deng, K Bello, B Aragam, P Ravikumar. (ICML 2023)  |  Code  |  Proceedings

8. A simple approach to balance task loss in multi-task learning. S Liang, C Deng, Y Zhang. (IEEE Big Data 2021)

9. Deep multi-task augmented feature learning via hierarchical graph neural network. P Guo, C Deng, L Xu, X Huang, Y Zhang. (ECML PKDD 2021)

• Booth Econometrics and Statistics Colloquium
• Booth Operation Management Colloquium
• UChicago Statistics Colloquium
• AI Workshop
• TTIC Colloquium

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