Strategic Research Areas
Smart Society
Hong Kong and the Greater Bay Area are ideal for smart society research, using the 2.5 quintillion bytes of data generated daily from sensors and devices. This data can enhance healthcare, infrastructure, and sustainability. By combining data from HKU’s Faculties of Architecture, Dentistry, Education, Engineering, Medicine, and Social Sciences with ICT, IoT, and citizen input, we can create a sustainable society. The “Smart Society” category includes three areas:
Publications & Projects
- Jingfeng Wu*, Difan Zou*, Vladimir Braverman, Quanquan Gu, Sham M. Kakade, Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. Proceedings of the 39th International Conference on Machine Learning. (2022) [Long Presentation]
Difan Zou*, Jingfeng Wu*, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade. The Benefit of Implicit Regularization from SGD in Least Square Problems. Conference on Advances in Neural Information Processing Systems. (2021) - Difan Zou*, Jingfeng Wu*, Vladimir Braverman, Quanquan Gu, Sham M. Kakade. Benign Overfitting of Constant-Stepsize SGD for Linear Regression. Annual Conference on Learning Theory. (2021)
- Difan Zou, Pan Xu, Quanquan Gu. Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. International Conference on Uncertainty in Artificial Intelligence. (2021)
- Difan Zou, Quanquan Gu. On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients. International Conference on Machine Learning. (2021)
Events
