Federated learning is an emerging distributed machine learning method,
e...
In federated learning (FL), a cluster of local clients are chaired under...
We focus on a class of non-smooth optimization problems over the Stiefel...
Sharpness aware minimization (SAM) optimizer has been extensively explor...
Safe reinforcement learning aims to learn the optimal policy while satis...
Modern deep neural networks for classification usually jointly learn a
b...
We consider a distributed non-convex optimization where a network of age...
We study the convergence properties of Riemannian gradient method for so...
Spectral clustering is one of the fundamental unsupervised learning meth...
We consider the problem of maximizing the ℓ_1 norm of a linear map over
...
The stochastic subgradient method is a widely-used algorithm for solving...
Nonsmooth Riemannian optimization is a still under explored subfield of
...
Sparse principal component analysis (PCA) and sparse canonical correlati...
This paper considers manifold optimization problems with nonsmooth and
n...
In this paper, we extend the geometric descent method recently proposed ...