1 citations · 2 across the 13 of their papers we have counts for
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Federated Learning on Riemannian Manifolds: A Gradient-Free Projection-Based Approach
Hongye Wang, Zhaoye Pan, Chang He +2
Federated learning (FL) has emerged as a powerful paradigm for collaborative model training across distributed clients while preserving data privacy. However, existing FL algorithm…
On Relatively Smooth Optimization over Riemannian Manifolds
Chang He, Jiaxiang Li, Bo Jiang +2
We study optimization over Riemannian embedded submanifolds, where the objective function is relatively smooth in the ambient Euclidean space. Such problems have broad applications…
Problem-Parameter-Free Decentralized Nonconvex Stochastic Optimization
Jiaxiang Li, Xuxing Chen, Shiqian Ma +1
Existing decentralized algorithms usually require knowledge of problem parameters for updating local iterates. For example, the hyperparameters (such as learning rate) usually requ…
Riemannian Bilevel Optimization
Jiaxiang Li, Shiqian Ma
In this work, we consider the bilevel optimization problem on Riemannian manifolds. We inspect the calculation of the hypergradient of such problems on general manifolds and thus e…
Zeroth-order Riemannian Averaging Stochastic Approximation Algorithms
Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma
We present Zeroth-order Riemannian Averaging Stochastic Approximation (\texttt{Zo-RASA}) algorithms for stochastic optimization on Riemannian manifolds. We show that \texttt{Zo-RAS…
A Riemannian ADMM
Jiaxiang Li, Shiqian Ma, Tejes Srivastava
We consider a class of Riemannian optimization problems where the objective is the sum of a smooth function and a nonsmooth function, considered in the ambient space. This class of…
Stochastic Zeroth-order Riemannian Derivative Estimation and Optimization
Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma
We consider stochastic zeroth-order optimization over Riemannian submanifolds embedded in Euclidean space, where the task is to solve Riemannian optimization problem with only nois…