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Mher Safaryan
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2020 – today
- 2024
- [c7]Rustem Islamov, Mher Safaryan, Dan Alistarh:
AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms. AISTATS 2024: 649-657 - [i14]Ionut-Vlad Modoranu, Mher Safaryan, Grigory Malinovsky, Eldar Kurtic, Thomas Robert, Peter Richtárik, Dan Alistarh:
MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence. CoRR abs/2405.15593 (2024) - [i13]Diyuan Wu, Ionut-Vlad Modoranu, Mher Safaryan, Denis Kuznedelev, Dan Alistarh:
The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information. CoRR abs/2408.17163 (2024) - [i12]Thomas Robert, Mher Safaryan, Ionut-Vlad Modoranu, Dan Alistarh:
LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics. CoRR abs/2410.16103 (2024) - 2023
- [j2]Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik, Mher Safaryan:
On Biased Compression for Distributed Learning. J. Mach. Learn. Res. 24: 276:1-276:50 (2023) - [j1]Rustem Islamov, Xun Qian, Slavomír Hanzely, Mher Safaryan, Peter Richtárik:
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation. Trans. Mach. Learn. Res. 2023 (2023) - [c6]Mher Safaryan, Alexandra Peste, Dan Alistarh:
Knowledge Distillation Performs Partial Variance Reduction. NeurIPS 2023 - [i11]Mher Safaryan
, Alexandra Peste, Dan Alistarh:
Knowledge Distillation Performs Partial Variance Reduction. CoRR abs/2305.17581 (2023) - [i10]Rustem Islamov, Mher Safaryan
AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms. CoRR abs/2310.20452 (2023) - 2022
- [c5]Xun Qian, Rustem Islamov
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning. AISTATS 2022: 680-720 - [c4]Mher Safaryan, Rustem Islamov
FedNL: Making Newton-Type Methods Applicable to Federated Learning. ICML 2022: 18959-19010 - [c3]Bokun Wang, Mher Safaryan, Peter Richtárik:
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques. NeurIPS 2022 - [i9]Rustem Islamov, Xun Qian, Slavomír Hanzely, Mher Safaryan, Peter Richtárik:
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation. CoRR abs/2206.03588 (2022) - [i8]Artavazd Maranjyan, Mher Safaryan
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity. CoRR abs/2210.16402 (2022) - 2021
- [c2]Mher Safaryan, Peter Richtárik:
Stochastic Sign Descent Methods: New Algorithms and Better Theory. ICML 2021: 9224-9234 - [c1]Mher Safaryan, Filip Hanzely, Peter Richtárik:
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization. NeurIPS 2021: 25688-25702 - [i7]Mher Safaryan
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization. CoRR abs/2102.07245 (2021) - [i6]Mher Safaryan
FedNL: Making Newton-Type Methods Applicable to Federated Learning. CoRR abs/2106.02969 (2021) - [i5]Bokun Wang, Mher Safaryan
Smoothness-Aware Quantization Techniques. CoRR abs/2106.03524 (2021) - [i4]Xun Qian
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning. CoRR abs/2111.01847 (2021) - 2020
- [i3]Mher Safaryan
Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor. CoRR abs/2002.08958 (2020) - [i2]Aleksandr Beznosikov
On Biased Compression for Distributed Learning. CoRR abs/2002.12410 (2020) - [i1]Alyazeed Albasyoni, Mher Safaryan
Optimal Gradient Compression for Distributed and Federated Learning. CoRR abs/2010.03246 (2020)
Coauthor Index
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last updated on 2024-11-27 20:32 CET by the dblp team
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