167 citations · 178 across the 2 of their papers we have counts for
3 papers
cs.LG2021★ 167 cited
A Field Guide to Federated Optimization
Jianyu Wang, Zachary Charles, Zheng Xu +50
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy prote…
cs.LG2019★ 11 cited
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop
Dmitry Kovalev, Samuel Horvath, Peter Richtarik
The stochastic variance-reduced gradient method (SVRG) and its accelerated variant (Katyusha) have attracted enormous attention in the machine learning community in the last few ye…
math.OC2018
Nonconvex Variance Reduced Optimization with Arbitrary Sampling
Samuel Horváth, Peter Richtárik
We provide the first importance sampling variants of variance reduced algorithms for empirical risk minimization with non-convex loss functions. In particular, we analyze non-conve…