SGD with Arbitrary Sampling: General Analysis and Improved Rates. - Xun Qian; Peter Richtárik; Robert M. Gower; Alibek Sailanbayev; Nicolas Loizou; Egor Shulgin (ICML, 2019)

SGD: General Analysis and Improved Rates. - Robert Mansel Gower; Nicolas Loizou; Xun Qian; Alibek Sailanbayev; Egor Shulgin; Peter Richtárik (CoRR, 2019)

Revisiting Stochastic Extragradient. - Konstantin Mishchenko; Dmitry Kovalev; Egor Shulgin; Peter Richtárik; Yura Malitsky (AISTATS, 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)

ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks. - Dmitry Kovalev; Egor Shulgin; Peter Richtárik; Alexander Rogozin; Alexander V. Gasnikov (ICML, 2021)

Adaptive Catalyst for Smooth Convex Optimization. - Anastasiya Ivanova; Dmitry Pasechnyuk; Dmitry Grishchenko; Egor Shulgin; Alexander V. Gasnikov; Vladislav Matyukhin (OPTIMA, 2021)

Towards Accelerated Rates for Distributed Optimization over Time-Varying Networks. - Alexander Rogozin; Vladislav Lukoshkin; Alexander V. Gasnikov; Dmitry Kovalev; Egor Shulgin (OPTIMA, 2021)

Shifted compression framework: generalizations and improvements. - Egor Shulgin; Peter Richtárik (UAI, 2022)

Certified Robustness in Federated Learning. - Motasem Alfarra; Juan C. Pérez; Egor Shulgin; Peter Richtárik; Bernard Ghanem (CoRR, 2022)

Towards a Better Theoretical Understanding of Independent Subnetwork Training. - Egor Shulgin; Peter Richtárik (ICML, 2024)

SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning. - Avetik G. Karagulyan; Egor Shulgin; Abdurakhmon Sadiev; Peter Richtárik (CoRR, 2024)

On the Convergence of DP-SGD with Adaptive Clipping. - Egor Shulgin; Peter Richtárik (CoRR, 2024)

MAST: model-agnostic sparsified training. - Yury Demidovich; Grigory Malinovsky; Egor Shulgin; Peter Richtárik (ICLR, 2025)

Smoothed Normalization for Efficient Distributed Private Optimization. - Egor Shulgin; Sarit Khirirat; Peter Richtárik (CoRR, 2025)

Gluon: Making Muon & Scion Great Again! (Bridging Theory and Practice of LMO-based Optimizers for LLMs). - Artem Riabinin; Egor Shulgin; Kaja Gruntkowska; Peter Richtárik (CoRR, 2025)