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)

Optimal Gradient Compression for Distributed and Federated Learning. - Alyazeed Albasyoni; Mher Safaryan; Laurent Condat; Peter Richtárik (CoRR, 2020)

Stochastic Sign Descent Methods: New Algorithms and Better Theory. - Mher Safaryan; Peter Richtárik (ICML, 2021)

Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization. - Mher Safaryan; Filip Hanzely; Peter Richtárik (NeurIPS, 2021)

Smoothness-Aware Quantization Techniques. - Bokun Wang; Mher Safaryan; Peter Richtárik (CoRR, 2021)

Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning. - Xun Qian; Rustem Islamov; Mher Safaryan; Peter Richtárik (AISTATS, 2022)

FedNL: Making Newton-Type Methods Applicable to Federated Learning. - Mher Safaryan; Rustem Islamov; Xun Qian; Peter Richtárik (ICML, 2022)

Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques. - Bokun Wang; Mher Safaryan; Peter Richtárik (NeurIPS, 2022)

Knowledge Distillation Performs Partial Variance Reduction. - Mher Safaryan; Alexandra Peste; Dan Alistarh (NeurIPS, 2023)

On Biased Compression for Distributed Learning. - Aleksandr Beznosikov; Samuel Horváth; Peter Richtárik; Mher Safaryan (J. Mach. Learn. Res., 2023)

Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation. - Rustem Islamov; Xun Qian; Slavomír Hanzely; Mher Safaryan; Peter Richtárik (Trans. Mach. Learn. Res., 2023)

AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms. - Rustem Islamov; Mher Safaryan; Dan Alistarh (AISTATS, 2024)

MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence. - Ionut-Vlad Modoranu; Mher Safaryan; Grigory Malinovsky; Eldar Kurtic; Thomas Robert 007; Peter Richtárik; Dan Alistarh (NeurIPS, 2024)

The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information. - Diyuan Wu; Ionut-Vlad Modoranu; Mher Safaryan; Denis Kuznedelev; Dan Alistarh (NeurIPS, 2024)

LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics. - Thomas Robert 007; Mher Safaryan; Ionut-Vlad Modoranu; Dan Alistarh (ICLR, 2025)

GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity. - Arto Maranjyan; Mher Safaryan; Peter Richtárik (Trans. Mach. Learn. Res., 2025)

SVD-Free Low-Rank Adaptive Gradient Optimization for Large Language Models. - Ionut-Vlad Modoranu; Mher Safaryan; Erik Schultheis; Dan Alistarh (CoRR, 2025)

DES-LOC: Desynced Low Communication Adaptive Optimizers for Training Foundation Models. - Alex Iacob; Lorenzo Sani; Mher Safaryan; Paris Giampouras; Samuel Horváth; Andrej Jovanovic; Meghdad Kurmanji; Preslav Aleksandrov; William F. Shen; Xinchi Qiu; Nicholas D. Lane (CoRR, 2025)

Unified Scaling Laws for Compressed Representations. - Andrei Panferov; Alexandra Volkova; Ionut-Vlad Modoranu; Vage Egiazarian; Mher Safaryan; Dan Alistarh (CoRR, 2025)

MT-DAO: Multi-Timescale Distributed Adaptive Optimizers with Local Updates. - Alex Iacob; Andrej Jovanovic; Mher Safaryan; Meghdad Kurmanji; Lorenzo Sani; Samuel Horváth; William F. Shen; Xinchi Qiu; Nicholas D. Lane (CoRR, 2025)

CAGE: Curvature-Aware Gradient Estimation For Accurate Quantization-Aware Training. - Soroush Tabesh; Mher Safaryan; Dan Alistarh (CoRR, 2025)