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09/18/2023
FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data
Federated learning is an emerging distributed machine learning method, e...
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05/19/2023
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape
In federated learning (FL), a cluster of local clients are chaired under...
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03/31/2023
Decentralized Weakly Convex Optimization Over the Stiefel Manifold
We focus on a class of non-smooth optimization problems over the Stiefel...
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03/01/2023
AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive Learning Rate and Momentum for Training Deep Neural Networks
Sharpness aware minimization (SAM) optimizer has been extensively explor...
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05/24/2022
Penalized Proximal Policy Optimization for Safe Reinforcement Learning
Safe reinforcement learning aims to learn the optimal policy while satis...
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03/17/2022
Do We Really Need a Learnable Classifier at the End of Deep Neural Network?
Modern deep neural networks for classification usually jointly learn a b...
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02/14/2021
Decentralized Riemannian Gradient Descent on the Stiefel Manifold
We consider a distributed non-convex optimization where a network of age...
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01/22/2021
On the Local Linear Rate of Consensus on the Stiefel Manifold
We study the convergence properties of Riemannian gradient method for so...
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07/18/2020
A Manifold Proximal Linear Method for Sparse Spectral Clustering with Application to Single-Cell RNA Sequencing Data Analysis
Spectral clustering is one of the fundamental unsupervised learning meth...
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05/05/2020
Manifold Proximal Point Algorithms for Dual Principal Component Pursuit and Orthogonal Dictionary Learning
We consider the problem of maximizing the â„“_1 norm of a linear map over ...
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04/28/2020
Distributed Projected Subgradient Method for Weakly Convex Optimization
The stochastic subgradient method is a widely-used algorithm for solving...
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11/12/2019
Nonsmooth Optimization over Stiefel Manifold: Riemannian Subgradient Methods
Nonsmooth Riemannian optimization is a still under explored subfield of ...
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03/27/2019
An Alternating Manifold Proximal Gradient Method for Sparse PCA and Sparse CCA
Sparse principal component analysis (PCA) and sparse canonical correlati...
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11/02/2018
Proximal Gradient Method for Manifold Optimization
This paper considers manifold optimization problems with nonsmooth and n...
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12/29/2016
