13 citations · 57 across the 29 of their papers we have counts for
4 papers · 1 filter
Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization
Yury Demidovich, Petr Ostroukhov, Grigory Malinovsky +4
Non-convex Machine Learning problems typically do not adhere to the standard smoothness assumption. Based on empirical findings, Zhang et al. (2020b) proposed a more realistic gene…
Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation
Grigory Malinovsky, Umberto Michieli, Hasan Abed Al Kader Hammoud +4
Fine-tuning has become a popular approach to adapting large foundational models to specific tasks. As the size of models and datasets grows, parameter-efficient fine-tuning techniq…
MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence
Ionut-Vlad Modoranu, Mher Safaryan, Grigory Malinovsky +4
We propose a new variant of the Adam optimizer called MicroAdam that specifically minimizes memory overheads, while maintaining theoretical convergence guarantees. We achieve this…
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction
Yury Demidovich, Grigory Malinovsky, Peter Richtárik
In this study, we investigate stochastic optimization on Riemannian manifolds, focusing on the crucial variance reduction mechanism used in both Euclidean and Riemannian settings.…