Rustem Islamov

Orcid: 0000-0002-5462-4711

According to our database1, Rustem Islamov authored at least 17 papers between 2021 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Links

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Bibliography

2025
On the Interaction of Noise, Compression Role, and Adaptivity under (L<sub>0</sub>, L<sub>1</sub>)-Smoothness: An SDE-based Approach.
CoRR, June, 2025

Safe-EF: Error Feedback for Nonsmooth Constrained Optimization.
CoRR, May, 2025

Double Momentum and Error Feedback for Clipping with Fast Rates and Differential Privacy.
CoRR, February, 2025

Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity.
Trans. Mach. Learn. Res., 2025

Towards Faster Decentralized Stochastic Optimization with Communication Compression.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Adaptive Methods through the Lens of SDEs: Theoretical Insights on the Role of Noise.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024
Near Optimal Decentralized Optimization with Compression and Momentum Tracking.
CoRR, 2024

Loss Landscape Characterization of Neural Networks without Over-Parametrization.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

EControl: Fast Distributed Optimization with Compression and Error Control.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Adaptive Compression for Communication-Efficient Distributed Training.
Trans. Mach. Learn. Res., 2023

Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation.
Trans. Mach. Learn. Res., 2023

Clip21: Error Feedback for Gradient Clipping.
CoRR, 2023

2022
FedNL: Making Newton-Type Methods Applicable to Federated Learning.
Proceedings of the International Conference on Machine Learning, 2022

Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Distributed Second Order Methods with Fast Rates and Compressed Communication.
Proceedings of the 38th International Conference on Machine Learning, 2021


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