7 citations · 13 across the 11 of their papers we have counts for
7 papers · 1 filter
Differentially Private Random Block Coordinate Descent
Artavazd Maranjyan, Abdurakhmon Sadiev, Peter Richtárik
Coordinate Descent (CD) methods have gained significant attention in machine learning due to their effectiveness in solving high-dimensional problems and their ability to decompose…
Speeding up Stochastic Proximal Optimization in the High Hessian Dissimilarity Setting
Elnur Gasanov, Peter Richtárik
Stochastic proximal point methods have recently garnered renewed attention within the optimization community, primarily due to their desirable theoretical properties. Notably, thes…
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…
On the Convergence of FedProx with Extrapolation and Inexact Prox
Hanmin Li, Peter Richtárik
Enhancing the FedProx federated learning algorithm (Li et al., 2020) with server-side extrapolation, Li et al. (2024a) recently introduced the FedExProx method. Their theoretical a…
Tighter Performance Theory of FedExProx
Wojciech Anyszka, Kaja Gruntkowska, Alexander Tyurin +1
We revisit FedExProx - a recently proposed distributed optimization method designed to enhance convergence properties of parallel proximal algorithms via extrapolation. In the proc…
MindFlayer SGD: Efficient Parallel SGD in the Presence of Heterogeneous and Random Worker Compute Times
Artavazd Maranjyan, Omar Shaikh Omar, Peter Richtárik
We investigate the problem of minimizing the expectation of smooth nonconvex functions in a distributed setting with multiple parallel workers that are able to compute stochastic g…