I am a Ph.D. candidate at Stanford ICME. My current research interest lies in machine learning thoery, in particular Federated Learning, Optimization and Deep Learning theory. I am fortunate to be advised by Professor Tengyu Ma.
Before coming to Stanford, I graduated from Peking University with B.S. degrees in Computational Mathematics and Computer Science.
Please find my CV here.
Honglin Yuan, Manzil Zaheer, Sashank Reddi, Federated Composite Optimization, arXiv:2011.08474
Honglin Yuan, Tengyu Ma, Federated Accelerated Stochastic Gradient Descent, NeurIPS 2020; Best Paper Award in International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020 (FL-ICML’20)
Honglin Yuan, Xiaoyi Gu, Rongjie Lai, Zaiwen Wen, Global Optimization with Orthogonality Constraints via Stochastic Diffusion on Manifold, Journal of Scientific Computing, 2019.