| 2026 | ITP | Lean Formalization of Generalization Error Bound by Rademacher Complexity and Dudley's Entropy Integral. | Sho Sonoda, Kazumi Kasaura, Yuma Mizuno, Kei Tsukamoto, Naoto Onda |
| 2025 | ICML | Deep Ridgelet Transform and Unified Universality Theorem for Deep and Shallow Joint-Group-Equivariant Machines. | Sho Sonoda, Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda |
| 2024 | ICLR | Koopman-based generalization bound: New aspect for full-rank weights. | Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Atsushi Nitanda, Taiji Suzuki |
| 2023 | ICML | How Powerful are Shallow Neural Networks with Bandlimited Random Weights? | Ming Li, Sho Sonoda, Feilong Cao, Yu Guang Wang, Jiye Liang |
| 2023 | ICML | Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation. | Hayata Yamasaki, Sathyawageeswar Subramanian, Satoshi Hayakawa, Sho Sonoda |
| 2022 | ICML | Fully-Connected Network on Noncompact Symmetric Space and Ridgelet Transform based on Helgason-Fourier Analysis. | Sho Sonoda, Isao Ishikawa, Masahiro Ikeda |
| 2021 | AISTATS | Ridge Regression with Over-parametrized Two-Layer Networks Converge to Ridgelet Spectrum. | Sho Sonoda, Isao Ishikawa, Masahiro Ikeda |
| 2014 | ICANN | Sampling Hidden Parameters from Oracle Distribution. | Sho Sonoda, Noboru Murata |