2 citations · 3 across the 3 of their papers we have counts for
3 papers
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…
stat.ML2021★ 1 cited
On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting
Shunta Akiyama, Taiji Suzuki
Deep learning empirically achieves high performance in many applications, but its training dynamics has not been fully understood theoretically. In this paper, we explore theoretic…
stat.ML2020★ 2 cited
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods
Taiji Suzuki, Shunta Akiyama
Establishing a theoretical analysis that explains why deep learning can outperform shallow learning such as kernel methods is one of the biggest issues in the deep learning literat…