Konstantin Burlachenko

23 papers A* 4Journal 17Unranked 2
YearRankTypeTitle / Venue / Authors
2025 J jnl
BurTorch: Revisiting Training from First Principles by Coupling Autodiff, Math Optimization, and Systems.
CoRR
Konstantin Burlachenko, Peter Richtárik
2025 J jnl
Optimization Methods and Software for Federated Learning.
CoRR
Konstantin Burlachenko
2024 A* conf
Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences.
NeurIPS
Abdurakhmon Sadiev, Grigory Malinovsky, Eduard Gorbunov, Igor Sokolov, Ahmed Khaled, Konstantin Burlachenko, Peter Richtárik
2024 A* conf
Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants.
ICLR
Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko
2024 J jnl
Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants.
CoRR
Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko
2024 J jnl
Faster Rates for Compressed Federated Learning with Client-Variance Reduction.
SIAM J. Math. Data Sci.
Haoyu Zhao, Konstantin Burlachenko, Zhize Li, Peter Richtárik
2024 A* conf
PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression.
NeurIPS
Vladimir Malinovskii, Denis Mazur, Ivan Ilin, Denis Kuznedelev, Konstantin Burlachenko, Kai Yi, Dan Alistarh, Peter Richtárik
2024 J jnl
PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression.
CoRR
Vladimir Malinovskii, Denis Mazur, Ivan Ilin, Denis Kuznedelev, Konstantin Burlachenko, Kai Yi, Dan Alistarh, Peter Richtárik
2024 J jnl
Unlocking FedNL: Self-Contained Compute-Optimized Implementation.
CoRR
Konstantin Burlachenko, Peter Richtárik
2023 J jnl
Error Feedback Shines when Features are Rare.
CoRR
Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko
2023 — conf
Federated Learning is Better with Non-Homomorphic Encryption.
DistributedML@CoNEXT
Konstantin Burlachenko, Abdulmajeed Alrowithi, Fahad Ali Albalawi, Peter Richtárik
2023 J jnl
Federated Learning is Better with Non-Homomorphic Encryption.
CoRR
Konstantin Burlachenko, Abdulmajeed Alrowithi, Fahad Ali Albalawi, Peter Richtárik
2023 J jnl
Federated Learning with Regularized Client Participation.
CoRR
Grigory Malinovsky, Samuel Horváth, Konstantin Burlachenko, Peter Richtárik
2023 J jnl
Personalized Federated Learning with Communication Compression.
Trans. Mach. Learn. Res.
El Houcine Bergou, Konstantin Burlachenko, Aritra Dutta, Peter Richtárik
2023 J jnl
Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling.
Trans. Mach. Learn. Res.
Alexander Tyurin, Lukang Sun, Konstantin Burlachenko, Peter Richtárik
2022 J jnl
FL_PyTorch: optimization research simulator for federated learning.
CoRR
Konstantin Burlachenko, Samuel Horváth, Peter Richtárik
2022 J jnl
Federated Optimization Algorithms with Random Reshuffling and Gradient Compression.
CoRR
Abdurakhmon Sadiev, Grigory Malinovsky, Eduard Gorbunov, Igor Sokolov, Ahmed Khaled, Konstantin Burlachenko, Peter Richtárik
2022 J jnl
Personalized Federated Learning with Communication Compression.
CoRR
El Houcine Bergou, Konstantin Burlachenko, Aritra Dutta, Peter Richtárik
2022 J jnl
Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling.
CoRR
Alexander Tyurin, Lukang Sun, Konstantin Burlachenko, Peter Richtárik
2021 — conf
FL_PyTorch: optimization research simulator for federated learning.
DistributedML@CoNEXT
Konstantin Burlachenko, Samuel Horváth, Peter Richtárik
2021 J jnl
Faster Rates for Compressed Federated Learning with Client-Variance Reduction.
CoRR
Haoyu Zhao, Konstantin Burlachenko, Zhize Li, Peter Richtárik
2021 A* conf
MARINA: Faster Non-Convex Distributed Learning with Compression.
ICML
Eduard Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtárik
2021 J jnl
MARINA: Faster Non-Convex Distributed Learning with Compression.
CoRR
Eduard Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtárik