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2020 – today
- 2024
[c5]Kazusato Oko, Shunta Akiyama, Denny Wu, Tomoya Murata, Taiji Suzuki:
SILVER: Single-loop variance reduction and application to federated learning. ICML 2024- 2023
- [c4]Shunta Akiyama, Taiji Suzuki:
Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods. ICLR 2023 - [c3]Kazusato Oko, Shunta Akiyama, Taiji Suzuki:
Diffusion Models are Minimax Optimal Distribution Estimators. ICML 2023: 26517-26582 - [i6]Kazusato Oko, Shunta Akiyama, Taiji Suzuki:
Diffusion Models are Minimax Optimal Distribution Estimators. CoRR abs/2303.01861 (2023) - 2022
- [i5]Shunta Akiyama, Taiji Suzuki:
Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods. CoRR abs/2205.14818 (2022) - [i4]Kazusato Oko, Shunta Akiyama, Tomoya Murata, Taiji Suzuki:
Versatile Single-Loop Method for Gradient Estimator: First and Second Order Optimality, and its Application to Federated Learning. CoRR abs/2209.00361 (2022) - [i3]Shunta Akiyama, Mitsuaki Obara, Yasushi Kawase:
Optimal design of lottery with cumulative prospect theory. CoRR abs/2209.00822 (2022) - 2021
- [c2]Taiji Suzuki, Shunta Akiyama:
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods. ICLR 2021 - [c1]Shunta Akiyama, Taiji Suzuki:
On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting. ICML 2021: 152-162 - [i2]Shunta Akiyama, Taiji Suzuki:
On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting. CoRR abs/2106.06251 (2021) - 2020
- [i1]Taiji Suzuki, Shunta Akiyama:
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods. CoRR abs/2012.03224 (2020)
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