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Recent Theoretical Advances in Non-Convex Optimization
Motivated by recent increased interest in optimization algorithms for no...
12/11/2020 ∙ by Marina Danilova, et al. ∙
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Local SGD: Unified Theory and New Efficient Methods
We present a unified framework for analyzing local SGD methods in the co...
11/03/2020 ∙ by Eduard Gorbunov, et al. ∙
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Linearly Converging Error Compensated SGD
In this paper, we propose a unified analysis of variants of distributed ...
10/23/2020 ∙ by Eduard Gorbunov, et al. ∙
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Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping
In this paper, we propose a new accelerated stochastic first-order metho...
05/21/2020 ∙ by Eduard Gorbunov, et al. ∙
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A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent
In this paper we introduce a unified analysis of a large family of varia...
05/27/2019 ∙ by Eduard Gorbunov, et al. ∙
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Distributed Learning with Compressed Gradient Differences
Training very large machine learning models requires a distributed compu...
01/26/2019 ∙ by Konstantin Mishchenko, et al. ∙
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An Accelerated Directional Derivative Method for Smooth Stochastic Convex Optimization
We consider smooth stochastic convex optimization problems in the contex...
04/08/2018 ∙ by Pavel Dvurechensky, et al. ∙
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An Accelerated Method for Derivative-Free Smooth Stochastic Convex Optimization
We consider an unconstrained problem of minimization of a smooth convex ...
02/25/2018 ∙ by Pavel Dvurechensky, et al. ∙
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