About
My research interests lie on designing principled machine learning methods. Currently, I mainly focus on three major themes:
- Reinforcement learning: design effective algorithms by exploiting the intrinsic structures in the uncertain dynamics for automatic decision making.
- Structured input and output: build effective models for capturing the structures information in input and output, e.g., binaries, sequences, programs, trees, and graphs.
- Large-scale nonparametric machine learning: develop efficient algorithms for machine learning methods, especially nonparametric methods, to handle hundreds of millions of data.