Graduate School of Information Science and Technology, The University of Tokyo, Japan; Graduate School of Information Science and Technology, The University of Tokyo, Japan; Center for Advanced Intelligence Project, RIKEN, Japan
Taiji Suzuki, Akiyama Shunta
Excess risk;minimax optimal rate;local Rademacher complexity;fast learning rate;kernel method;linear estimator
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods