Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods. - Taiji Suzuki; Shunta Akiyama (ICLR, 2021)

On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting. - Shunta Akiyama; Taiji Suzuki (ICML, 2021)

Versatile Single-Loop Method for Gradient Estimator: First and Second Order Optimality, and its Application to Federated Learning. - Kazusato Oko; Shunta Akiyama; Tomoya Murata; Taiji Suzuki (CoRR, 2022)

Optimal design of lottery with cumulative prospect theory. - Shunta Akiyama; Mitsuaki Obara; Yasushi Kawase (CoRR, 2022)

Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods. - Shunta Akiyama; Taiji Suzuki (ICLR, 2023)

Diffusion Models are Minimax Optimal Distribution Estimators. - Kazusato Oko; Shunta Akiyama; Taiji Suzuki (ICML, 2023)

SILVER: Single-loop variance reduction and application to federated learning. - Kazusato Oko; Shunta Akiyama; Denny Wu; Tomoya Murata; Taiji Suzuki (ICML, 2024)