| 2025 | AISTATS | Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs. | Enea Monzio Compagnoni, Rustem Islamov, Frank Norbert Proske, Aurlien Lucchi |
| 2025 | ICLR | 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, Aurlien Lucchi |
| 2025 | ICLR | Towards Faster Decentralized Stochastic Optimization with Communication Compression. | Rustem Islamov, Yuan Gao, Sebastian U. Stich |
| 2025 | ICML | Safe-EF: Error Feedback for Non-smooth Constrained Optimization. | Rustem Islamov, Yarden As, Ilyas Fatkhullin |
| 2024 | AISTATS | AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms. | Rustem Islamov, Mher Safaryan, Dan Alistarh |
| 2024 | ICLR | EControl: Fast Distributed Optimization with Compression and Error Control. | Yuan Gao, Rustem Islamov, Sebastian U. Stich |
| 2022 | AISTATS | Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning. | Xun Qian, Rustem Islamov, Mher Safaryan, Peter Richtrik |
| 2022 | ICML | FedNL: Making Newton-Type Methods Applicable to Federated Learning. | Mher Safaryan, Rustem Islamov, Xun Qian, Peter Richtrik |
| 2021 | ICML | Distributed Second Order Methods with Fast Rates and Compressed Communication. | Rustem Islamov, Xun Qian, Peter Richtrik |