Mher Safaryan | DeepAI

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∙ 05/27/2023

Knowledge Distillation Performs Partial Variance Reduction

Knowledge distillation is a popular approach for enhancing the performan...

0 Mher Safaryan, et al. ∙

∙ 10/28/2022

GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity

In this work, we study distributed optimization algorithms that reduce t...

0 Artavazd Maranjyan, et al. ∙

∙ 06/07/2022

Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation

Despite their high computation and communication costs, Newton-type meth...

0 Rustem Islamov, et al. ∙

∙ 11/02/2021

Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning

Recent advances in distributed optimization have shown that Newton-type ...

1 Xun Qian, et al. ∙

∙ 06/07/2021

Smoothness-Aware Quantization Techniques

Distributed machine learning has become an indispensable tool for traini...

0 Bokun Wang, et al. ∙

∙ 06/05/2021

FedNL: Making Newton-Type Methods Applicable to Federated Learning

Inspired by recent work of Islamov et al (2021), we propose a family of ...

0 Mher Safaryan, et al. ∙

∙ 02/14/2021

Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization

Large scale distributed optimization has become the default tool for the...

16 Mher Safaryan, et al. ∙

∙ 10/07/2020

Optimal Gradient Compression for Distributed and Federated Learning

Communicating information, like gradient vectors, between computing node...

0 Alyazeed Albasyoni, et al. ∙

∙ 02/27/2020

On Biased Compression for Distributed Learning

In the last few years, various communication compression techniques have...

15 Aleksandr Beznosikov, et al. ∙

∙ 02/20/2020

Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor

In order to mitigate the high communication cost in distributed and fede...

13 Mher Safaryan, et al. ∙

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