Egor Shulgin

23 papers A* 4A 2Journal 15Unranked 2
YearRankTypeTitle / Venue / Authors
2026 J jnl
Deriving Hyperparameter Scaling Laws via Modern Optimization Theory.
CoRR
Egor Shulgin, Dimitri von Rütte, Tianyue H. Zhang, Niccolò Ajroldi, Bernhard Schölkopf, Antonio Orvieto
2025 J jnl
Beyond the Ideal: Analyzing the Inexact Muon Update.
CoRR
Egor Shulgin, Sultan AlRashed, Francesco Orabona, Peter Richtárik
2025 J jnl
First Provable Guarantees for Practical Private FL: Beyond Restrictive Assumptions.
CoRR
Egor Shulgin, Grigory Malinovsky, Sarit Khirirat, Peter Richtárik
2025 J jnl
Gluon: Making Muon & Scion Great Again! (Bridging Theory and Practice of LMO-based Optimizers for LLMs).
CoRR
Artem Riabinin, Egor Shulgin, Kaja Gruntkowska, Peter Richtárik
2025 A* conf
MAST: model-agnostic sparsified training.
ICLR
Yury Demidovich, Grigory Malinovsky, Egor Shulgin, Peter Richtárik
2025 J jnl
Smoothed Normalization for Efficient Distributed Private Optimization.
CoRR
Egor Shulgin, Sarit Khirirat, Peter Richtárik
2024 J jnl
On the Convergence of DP-SGD with Adaptive Clipping.
CoRR
Egor Shulgin, Peter Richtárik
2024 J jnl
SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning.
CoRR
Avetik G. Karagulyan, Egor Shulgin, Abdurakhmon Sadiev, Peter Richtárik
2024 A* conf
Towards a Better Theoretical Understanding of Independent Subnetwork Training.
ICML
Egor Shulgin, Peter Richtárik
2023 J jnl
MAST: Model-Agnostic Sparsified Training.
CoRR
Yury Demidovich, Grigory Malinovsky, Egor Shulgin, Peter Richtárik
2023 J jnl
Towards a Better Theoretical Understanding of Independent Subnetwork Training.
CoRR
Egor Shulgin, Peter Richtárik
2022 J jnl
Certified Robustness in Federated Learning.
CoRR
Motasem Alfarra, Juan C. Pérez, Egor Shulgin, Peter Richtárik, Bernard Ghanem
2022 J jnl
Shifted Compression Framework: Generalizations and Improvements.
CoRR
Egor Shulgin, Peter Richtárik
2022 A conf
Shifted compression framework: generalizations and improvements.
UAI
Egor Shulgin, Peter Richtárik
2021 A* conf
ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks.
ICML
Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Alexander Rogozin, Alexander V. Gasnikov
2021 J jnl
ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks.
CoRR
Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Alexander Rogozin, Alexander V. Gasnikov
2021 — conf
Adaptive Catalyst for Smooth Convex Optimization.
OPTIMA
Anastasiya Ivanova, Dmitry Pasechnyuk, Dmitry Grishchenko, Egor Shulgin, Alexander V. Gasnikov, Vladislav Matyukhin
2021 — conf
Towards Accelerated Rates for Distributed Optimization over Time-Varying Networks.
OPTIMA
Alexander Rogozin, Vladislav Lukoshkin, Alexander V. Gasnikov, Dmitry Kovalev, Egor Shulgin
2020 A conf
Revisiting Stochastic Extragradient.
AISTATS
Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Yura Malitsky
2020 J jnl
Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor.
CoRR
Mher Safaryan, Egor Shulgin, Peter Richtárik
2019 J jnl
Revisiting Stochastic Extragradient.
CoRR
Konstantin Mishchenko, Dmitry Kovalev, Egor Shulgin, Peter Richtárik, Yura Malitsky
2019 A* conf
SGD with Arbitrary Sampling: General Analysis and Improved Rates.
ICML
Xun Qian, Peter Richtárik, Robert M. Gower, Alibek Sailanbayev, Nicolas Loizou, Egor Shulgin
2019 J jnl
SGD: General Analysis and Improved Rates.
CoRR
Robert Mansel Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, Peter Richtárik