Five Strategic Research Areas
Fundamentals of AI & Data Science
The Fundamentals of AI and Data Science drive innovation by analyzing large datasets and mimicking human intelligence. Data Science extracts insights, while AI develops systems that learn and adapt, improving decision-making across industries like healthcare and finance.
HKU IDS excels through interdisciplinary, collaborative research, integrating advanced techniques to address issues like bias and data privacy. Our holistic approach promotes ethical innovation and sustainable development, enabling researchers and partners to tackle complex challenges and ensure technology benefits society responsibly.
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 & Seminars
