Zhongzhu Chen, Marcia Fampa, Amélie Lambert, Jon Lee. (2021). "Mixing convex-optimization bounds for maximum-entropy sampling". Mathematical Programming, Series B.
Zhongzhu Chen, Marcia Fampa, Jon Lee. (2022). "Technical Note—Masking Anstreicher’s linx Bound for Improved Entropy Bounds" . Operations Research.
Zhongzhu Chen, Marcia Fampa, Jon Lee. (2022). "On computing with some convex relaxations for the maximum-entropy sampling problem". Informs Journal on Computing.
Chaowei Xiao*, Zhongzhu Chen*, Kun Jin*, Jiongxiao Wang*, Weili Nie, Mingyan Liu, Anima Anandkumar, Bo Li, Dawn Song. (2022+). "DensePure: Understanding Diffusion Models Towards Adversarial Learning." Eleventh International Conference on Learning Representations (ICLR), 2023.
* denotes equal contribution
1. Zhongzhu Chen, Marcia Fampa, Jon Lee. "Generalized Scaling for the Constrained Maximum-Entropy Sampling Problem".
2. Jiawei Zhang*, Zhongzhu Chen*, Huan Zhang, Chaowei Xiao, Bo Li. "DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local Smoothing".
3. Kun Jin*, Tongxin Yin*, Zhongzhu Chen*, Zeyu Sun, Xueru Zhang, Yang Liu, Mingyan Liu. "Performative Federated Learning".
* denotes equal constribution
(Working) Zhongzhu Chen, Marcia Fampa, Jon Lee. (2022+). "Exploring Variable Fixing for Mixed Integer Nonlinear Programming: Optimal Dual solution is Not Always the Best"
(Working) Zhongzhu Chen, Marcia Fampa, Jon Lee. (2022+). "A limited-memory quasi-Newton method for mask optimization on Anstreicher’s linx bound"
(Working) Zhongzhu Chen, Jiongxiao Wang, Kun Jin, Chaowei Xiao. (2022+). "Certified Robustness of Diffusion Model on Long-Tail Distribution"