Five Strategic Research Areas
Intelligent Robotics & Systems
Our research in Intelligent Robotics focuses on advancing robotic manipulation and humanoid technologies toward artificial general intelligence. We use cutting-edge AI, combining Foundation Models with Reinforcement and Imitation Learning, to enable robots to perform complex tasks and interact socially, evolving through autonomous exploration.
Through international collaboration, HKU IDS addresses key challenges like the simulation-to-reality gap and scalability for real-world applications. Our work enhances robot efficiency and adaptability, impacting healthcare, retail, and manufacturing, and aims to make robots indispensable partners in solving global challenges and transforming industries.
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
