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05/25/2023
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method
This paper proposes a new easy-to-implement parameter-free gradient-base...
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09/06/2022
Faster federated optimization under second-order similarity
Federated learning (FL) is a subfield of machine learning where multiple...
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11/22/2021
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning
Federated Learning (FL) is an increasingly popular machine learning para...
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02/12/2021
Proximal and Federated Random Reshuffling
Random Reshuffling (RR), also known as Stochastic Gradient Descent (SGD)...
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06/20/2020
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization
We present a unified theorem for the convergence analysis of stochastic ...
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06/10/2020
Random Reshuffling: Simple Analysis with Vast Improvements
Random Reshuffling (RR) is an algorithm for minimizing finite-sum functi...
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02/09/2020
Better Theory for SGD in the Nonconvex World
Large-scale nonconvex optimization problems are ubiquitous in modern mac...
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12/20/2019
Distributed Fixed Point Methods with Compressed Iterates
We propose basic and natural assumptions under which iterative optimizat...
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09/10/2019
Better Communication Complexity for Local SGD
We revisit the local Stochastic Gradient Descent (local SGD) method and ...
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09/10/2019
Gradient Descent with Compressed Iterates
We propose and analyze a new type of stochastic first order method: grad...
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09/10/2019
