Oct 16, 2018 · We prove that constant step-size stochastic gradient descent (SGD) with Nesterov acceleration matches the convergence rate of the deterministic accelerated ...
Modern machine learning focuses on highly expressive models that are able to fit or inter- polate the data completely, resulting in zero training loss.
[PDF] Fast and Faster Convergence of SGD for Over-Parameterized Models
vaswanis.github.io › SR-poster
▷ We show that these results lead to a modified perceptron algorithm that has an accelerated rate of decrease on the number of mistakes. General Setup.
It is proved that constant step-size stochastic gradient descent (SGD) with Nesterov acceleration matches the convergence rate of the deterministic ...
Our goal is to go further in the analysis of the Stochastic Average Gradient Accelerated (SAGA) algorithm. To achieve this, we introduce a new $\lambda$-SAGA ...
Apr 5, 2019 · We used these results to demonstrate the fast convergence of the stochastic perceptron algorithm employing the squared-hinge loss. We showed ...
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron. S. Vaswani, F. Bach, and M. Schmidt. CoRR, (2018 ) ...
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Nov 9, 2023 · The paper studies the convergence of local SGD in an overparameterization setting where the model can interpolate the training examples.
Fast and Faster Convergence of SGD for Over-Parameterized Models (and an Accelerated Perceptron). Modern machine learning focuses on highly expressive models ...
The convergence of Local SGD (or FedAvg) for such over-parameterized models in the heterogeneous data setting is analyzed and improved upon the existing ...