Mher Safaryan

According to our database, Mher Safaryan authored at least 13 papers between 2020 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms.

Rustem Islamov, Mher Safaryan, Dan Alistarh

CoRR, 2023

Knowledge Distillation Performs Partial Variance Reduction.

Mher Safaryan, Alexandra Peste, Dan Alistarh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

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

Artavazd Maranjyan, Mher Safaryan, Peter Richtárik

CoRR, 2022

Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation.

Rustem Islamov, Xun Qian, Slavomír Hanzely, Mher Safaryan, Peter Richtárik

CoRR, 2022

Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques.

Bokun Wang, Mher Safaryan, Peter Richtárik

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

FedNL: Making Newton-Type Methods Applicable to Federated Learning.

Mher Safaryan, Rustem Islamov, Xun Qian, Peter Richtárik

Proceedings of the International Conference on Machine Learning, 2022

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

Xun Qian, Rustem Islamov, Mher Safaryan, Peter Richtárik

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Smoothness-Aware Quantization Techniques.

Bokun Wang, Mher Safaryan, Peter Richtárik

CoRR, 2021

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

Mher Safaryan, Filip Hanzely, Peter Richtárik

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Stochastic Sign Descent Methods: New Algorithms and Better Theory.

Mher Safaryan, Peter Richtárik

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Optimal Gradient Compression for Distributed and Federated Learning.

Alyazeed Albasyoni, Mher Safaryan, Laurent Condat, Peter Richtárik

CoRR, 2020

On Biased Compression for Distributed Learning.

Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik, Mher Safaryan

CoRR, 2020

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

Mher Safaryan, Egor Shulgin, Peter Richtárik

CoRR, 2020