I am currently a fourth-year Ph.D. student of Computer Science and Engineering at ILLIDAN Lab@MSU, advised by Dr. Jiayu Zhou. Previously, I obtained my B.S. in Physics and M.S. in Computer Science at USTC. My research interests include Federated Learning and Data Privacy. Check my curricula vitae.
MSc in Computer Science, 2018
University of Science and Technology of China
BSc in Physics, minor in CS., 2015
University of Science and Technology of China
A distributed domain/group debiasing framework for unsupervised domain adaptation or fairness enhancement.
In this paper, we focus on subspace-based learning problems, where data elements are linear subspaces instead of vectors. To handle this kind of data, Grassmann kernels were proposed to measure the space structure and used with classifiers, e.g., Support Vector Machines (SVMs). However, the existing discriminative algorithms mostly ignore the instability of subspaces, which would cause the classifiers to be misled by disturbed instances. Thus we propose considering all potential disturbances of subspaces in learning processes to obtain more robust classifiers.