192 citations · 220 across the 13 of their papers we have counts for
3 papers · 1 filter
SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning
Avetik Karagulyan, Egor Shulgin, Abdurakhmon Sadiev +1
Cross-device training is a crucial subfield of federated learning, where the number of clients can reach into the billions. Standard approaches and local methods are prone to issue…
Variance Reduced Distributed Non-Convex Optimization Using Matrix Stepsizes
Hanmin Li, Avetik Karagulyan, Peter Richtárik
Matrix-stepsized gradient descent algorithms have been shown to have superior performance in non-convex optimization problems compared to their scalar counterparts. The det-CGD alg…
Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization
Hanmin Li, Avetik Karagulyan, Peter Richtárik
This paper introduces a new method for minimizing matrix-smooth non-convex objectives through the use of novel Compressed Gradient Descent (CGD) algorithms enhanced with a matrix-v…