Distributed Second Order Methods with Fast Rates and Compressed Communication. - Rustem Islamov; Xun Qian; Peter Richtárik (ICML, 2021)

Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning. - Xun Qian; Rustem Islamov; Mher Safaryan; Peter Richtárik (AISTATS, 2022)

FedNL: Making Newton-Type Methods Applicable to Federated Learning. - Mher Safaryan; Rustem Islamov; Xun Qian; Peter Richtárik (ICML, 2022)

Clip21: Error Feedback for Gradient Clipping. - Sarit Khirirat; Eduard Gorbunov; Samuel Horváth; Rustem Islamov; Fakhri Karray; Peter Richtárik (CoRR, 2023)

Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation. - Rustem Islamov; Xun Qian; Slavomír Hanzely; Mher Safaryan; Peter Richtárik (Trans. Mach. Learn. Res., 2023)

Adaptive Compression for Communication-Efficient Distributed Training. - Maksim Makarenko; Elnur Gasanov; Abdurakhmon Sadiev; Rustem Islamov; Peter Richtárik (Trans. Mach. Learn. Res., 2023)

AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms. - Rustem Islamov; Mher Safaryan; Dan Alistarh (AISTATS, 2024)

EControl: Fast Distributed Optimization with Compression and Error Control. - Yuan Gao 025; Rustem Islamov; Sebastian U. Stich (ICLR, 2024)

Loss Landscape Characterization of Neural Networks without Over-Parametrization. - Rustem Islamov; Niccolò Ajroldi; Antonio Orvieto; Aurélien Lucchi (NeurIPS, 2024)

Near Optimal Decentralized Optimization with Compression and Momentum Tracking. - Rustem Islamov; Yuan Gao 025; Sebastian U. Stich (CoRR, 2024)

Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs. - Enea Monzio Compagnoni; Rustem Islamov; Frank Norbert Proske; Aurélien Lucchi (AISTATS, 2025)

Adaptive Methods through the Lens of SDEs: Theoretical Insights on the Role of Noise. - Enea Monzio Compagnoni; Tianlin Liu; Rustem Islamov; Frank Norbert Proske; Antonio Orvieto; Aurélien Lucchi (ICLR, 2025)

Towards Faster Decentralized Stochastic Optimization with Communication Compression. - Rustem Islamov; Yuan Gao 025; Sebastian U. Stich (ICLR, 2025)

Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity. - Konstantin Mishchenko; Rustem Islamov; Eduard Gorbunov; Samuel Horváth (Trans. Mach. Learn. Res., 2025)

Double Momentum and Error Feedback for Clipping with Fast Rates and Differential Privacy. - Rustem Islamov; Samuel Horváth; Aurélien Lucchi; Peter Richtárik; Eduard Gorbunov (CoRR, 2025)

Safe-EF: Error Feedback for Nonsmooth Constrained Optimization. - Rustem Islamov; Yarden As; Ilyas Fatkhullin (CoRR, 2025)

On the Interaction of Noise, Compression Role, and Adaptivity under (L0, L1)-Smoothness: An SDE-based Approach. - Enea Monzio Compagnoni; Rustem Islamov; Antonio Orvieto; Eduard Gorbunov (CoRR, 2025)