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cs.LG2022
Versatile Single-Loop Method for Gradient Estimator: First and Second Order Optimality, and its Application to Federated Learning
Kazusato Oko, Shunta Akiyama, Tomoya Murata +1
While variance reduction methods have shown great success in solving large scale optimization problems, many of them suffer from accumulated errors and, therefore, should periodica…
cs.LG2021
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning
Tomoya Murata, Taiji Suzuki
Recently, local SGD has got much attention and been extensively studied in the distributed learning community to overcome the communication bottleneck problem. However, the superio…
cs.LG2020
Gradient Descent in RKHS with Importance Labeling
Tomoya Murata, Taiji Suzuki
Labeling cost is often expensive and is a fundamental limitation of supervised learning. In this paper, we study importance labeling problem, in which we are given many unlabeled d…