Jiang Hu
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Postdoc at UC Berkeley. |
About me
I am a postdoc at the Department of Mathematics, University of California, Berkeley. My current research focuses on manifold optimization, nonsmooth optimization, decentralized optimization and federated learning, and their applications in machine learning and signal processing.
News
9/2024 One paper titled ‘‘Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data’’ is accepted by NeurIPS 2024. [link]
7/2024 Our manuscript titled ‘‘Improving the communication in decentralized manifold optimization through single-step consensus and compression’’ is on arxiv. [arxiv]
5/2024 One paper titled ‘‘Convergence analysis of an adaptively regularized natural gradient method’’ is accepted by IEEE TSP. [link]
4/2024 Honored to receive the Best Paper Award at ICASSP 2024, 1/2826. [link] [page]
2/2024 One paper titled ‘‘A projected semismooth Newton method for a class of nonconvex composite programs with strong prox-regularity’’ is accepted by JMLR. [link]
1/2024 One paper titled ‘‘Riemannian Natural Gradient Methods’’ is published at SISC. [link]