Decentralized personalized federated learning: Lower bounds and optimal algorithm for all personalization modes. - Abdurakhmon Sadiev; Ekaterina Borodich; Aleksandr Beznosikov; Darina Dvinskikh; Saveliy Chezhegov; Rachael Tappenden; Martin Takác 001; Alexander V. Gasnikov (EURO J. Comput. Optim., 2022)

Local Methods with Adaptivity via Scaling. - Saveliy Chezhegov; Sergey Skorik; Nikolas Khachaturov; Danil Shalagin; Aram Avetisyan; Aleksandr Beznosikov; Martin Takác 001; Yaroslav Kholodov; Alexander V. Gasnikov (CoRR, 2024)

Gradient Clipping Improves AdaGrad when the Noise Is Heavy-Tailed. - Saveliy Chezhegov; Yaroslav Klyukin; Andrei Semenov; Aleksandr Beznosikov; Alexander V. Gasnikov; Samuel Horváth; Martin Takác 001; Eduard Gorbunov (CoRR, 2024)

Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling. - Daniil Medyakov; Gleb Molodtsov; Saveliy Chezhegov; Alexey Rebrikov; Aleksandr Beznosikov (CoRR, 2025)

Convergence of Clipped-SGD for Convex (L0,L1)-Smooth Optimization with Heavy-Tailed Noise. - Saveliy Chezhegov; Aleksandr Beznosikov; Samuel Horváth; Eduard Gorbunov (CoRR, 2025)

Enhancing Stability of Physics-Informed Neural Network Training Through Saddle-Point Reformulation. - Dmitry Bylinkin; Mikhail Aleksandrov; Saveliy Chezhegov; Aleksandr Beznosikov (CoRR, 2025)