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2020 – today
- 2025
[c1]Savelii Chezhegov, Yaroslav Klyukin, Andrei Semenov, Aleksandr Beznosikov, Alexander V. Gasnikov, Samuel Horváth, Martin Takác, Eduard Gorbunov:
Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed. ICML 2025- [i7]Daniil Medyakov, Gleb Molodtsov, Saveliy Chezhegov, Alexey Rebrikov, Aleksandr Beznosikov:
Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling. CoRR abs/2502.14648 (2025) - [i6]Saveliy Chezhegov, Aleksandr Beznosikov, Samuel Horváth, Eduard Gorbunov:
Convergence of Clipped-SGD for Convex (L0,L1)-Smooth Optimization with Heavy-Tailed Noise. CoRR abs/2505.20817 (2025) - [i5]Dmitry Bylinkin, Mikhail Aleksandrov, Saveliy Chezhegov, Aleksandr Beznosikov:
Enhancing Stability of Physics-Informed Neural Network Training Through Saddle-Point Reformulation. CoRR abs/2507.16008 (2025) - [i4]Saleh Vatan Khah, Savelii Chezhegov, Shahrokh Farahmand, Samuel Horváth, Eduard Gorbunov:
Differentially Private Clipped-SGD: High-Probability Convergence with Arbitrary Clipping Level. CoRR abs/2507.23512 (2025) - [i3]Dmitrii Feoktistov, Igor Ignashin, Andrey Veprikov, Nikita Borovko, Alexander Bogdanov, Savelii Chezhegov, Aleksandr Beznosikov:
Aligning Distributionally Robust Optimization with Practical Deep Learning Needs. CoRR abs/2508.16734 (2025) - 2024
- [i2]Saveliy Chezhegov, Sergey Skorik, Nikolas Khachaturov, Danil Shalagin, Aram Avetisyan, Aleksandr Beznosikov, Martin Takác, Yaroslav Kholodov, Alexander V. Gasnikov:
Local Methods with Adaptivity via Scaling. CoRR abs/2406.00846 (2024) - [i1]Saveliy Chezhegov, Yaroslav Klyukin, Andrei Semenov
, Aleksandr Beznosikov, Alexander V. Gasnikov, Samuel Horváth, Martin Takác, Eduard Gorbunov:
Gradient Clipping Improves AdaGrad when the Noise Is Heavy-Tailed. CoRR abs/2406.04443 (2024) - 2022
- [j1]Abdurakhmon Sadiev, Ekaterina Borodich, Aleksandr Beznosikov, Darina Dvinskikh, Saveliy Chezhegov, Rachael Tappenden, Martin Takác, Alexander V. Gasnikov:
Decentralized personalized federated learning: Lower bounds and optimal algorithm for all personalization modes. EURO J. Comput. Optim. 10: 100041 (2022)
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