Zhou, Yi

Educational Background

- Nonconvex and distributed optimization
- Reinforcement learning
- Statistical learning theory
- Machine learning (ML) for exploratory data analysis (EDA) and computer-aided design (CAD)

Air Force Research Laboratory Visiting Faculty Research Program – 2024
Faculty Early Career Award, National Science Foundation Electrical, Communications and Cyber Systems – 2023
Spotlight paper award, NeurIPS – 2018

Y. Zhang, Y. Zhou, K. Ji, and M. Zavlanos., “Boosting one-point derivative-free online optimization via residual feedback,” in IEEE Transactions on Automatic Control (TAC), 2024.
Z. Chen, Y. Zhou, and H. Huang, “On the hardness of constrained cooperative multi-agent reinforcement learning,” in Proc. International Conference on Learning Representations, 2024.
Z. Guan, Y. Zhou, and Y. Liang, “On the hardness of online nonconvex optimization with single oracle feedback,” in Proc. International Conference on Learning Representations, 2024.
C. Morchdi, C.-H. Chiu, Y. Zhou, and T.-W. Huang, “A resource-efficient task scheduling system using reinforcement learning,” in Proc. Asia and South Pacific Design Automation Conference (ASP-DAC), 2024, pp. 89–95.
C. Chen, J. Zhou, J. Ding, and Y. Zhou, “Assisted learning for organizations with limited imbalanced data,” Transactions on Machine Learning Research (TMLR), 2023.