3 citations · 3 across the 3 of their papers we have counts for
12 papers
Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization
Grigory Malinovsky
Machine learning and optimization have advanced together, with practical demands motivating new theory and theoretical breakthroughs enabling new applications. Modern large-scale t…
TAMUNA: Doubly Accelerated Distributed Optimization under Partial Participation
Laurent Condat, Ivan Agarský, Grigory Malinovsky +1
In distributed optimization and federated learning, slow and costly communication between parallel devices and the central server constitutes the primary bottleneck. To alleviate t…
Distance-Aware Muon: Adaptive Step Scaling for Normalized Optimization
Yury Demidovich, Abhishek Chakraborty, Grigory Malinovsky +2
Muon and related normalized optimizers decouple the choice of update direction from the choice of step scale, but their practical performance remains sensitive to the scale of the…
Communication-Efficient Gluon in Federated Learning
Xun Qian, Alexander Gaponov, Grigory Malinovsky +1
Recent developments have shown that Muon-type optimizers based on linear minimization oracles (LMOs) over non-Euclidean norm balls have the potential to get superior practical perf…
Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions
Rustem Islamov, Grigory Malinovsky, Alexander Gaponov +3
Federated Learning (FL) enables heterogeneous clients to collaboratively train a shared model without centralizing their raw data, offering an inherent level of privacy. However, g…
Revisiting Stochastic Proximal Point Methods: Generalized Smoothness and Similarity
Zhirayr Tovmasyan, Grigory Malinovsky, Laurent Condat +1
The growing prevalence of nonsmooth optimization problems in machine learning has spurred significant interest in generalized smoothness assumptions. Among these, the (L0, L1)-smoo…