研究概要

主要論文

• 1. Juno Kim, Taiji Suzuki.:
  "Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape. Forty-first International Conference on Machine Learning (ICML2024),"
  Proceedings of Machine Learning Research, 235:24527--24561, (2024)
• 2. Kazusato Oko, Shunta Akiyama, Denny Wu, Tomoya Murata, Taiji Suzuki.:
  "SILVER: Single-loop variance reduction and application to federated learning. Forty-first International Conference on Machine Learning" (ICML2024)
  Proceedings of Machine Learning Research,　235:38683--38739, (2024)
• 3. Taiji Suzuki, Denny Wu, Atsushi Nitanda.:
  "Convergence of mean-fieldLangevin dynamics: Time and space discretization, stochastic gradient,and variance reduction."
  Thirty-seventh Conference on NeuralInformation Processing Systems (NeurIPS2023), pp. 15545--15577,(2023)
• 4. Taiji Suzuki, Denny Wu, Kazusato Oko, Atsushi Nitanda.:
  "Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond."
  Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS2023), pp. 34536--34556,(2023)
• 5. Kazusato Oko, Shunta Akiyama, Taiji Suzuki.:
  "Diffusion Models are Minimax Optimal Distribution Estimators. Proceedings of the 40th International Conference on Machine Learning (ICML2023)"
  Proceedings of Machine Learning Research, 202:26517--26582,(2023)
• 6. Shokichi Takakura, Taiji Suzuki.:
  "Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input. Proceedings of the 40th International Conference on Machine Learning (ICML2023)"
  Proceedings of Machine Learning Research, 202:33416--33447, (2023)
• 7. Atsushi Nitanda, Denny Wu, Taiji Suzuki.:
  "Convex Analysis of the Mean Field Langevin Dynamics. 25th International Conference on Artificial Intelligence and Statistics (AISTATS2022)"
  Proceedings of Machine Learning Research, 151:9741--9757,(2022)
• 8. Atsushi Nitanda, and Taiji Suzuki.:
  "Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime."
  ICLR2021 (ICLR2021 outstanding paper award).
• 9. Taiji Suzuki, Hiroshi Abe, Tomoya Murata, Shingo Horiuchi, Kotaro Ito, Tokuma Wachi, So Hirai, Masatoshi Yukishima, Tomoaki Nishimura.:
  "Spectral pruning: Compressing deep neural networks via spectral analysis and its generalization error"
  The 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI 2020)
• 10. Taiji Suzuki, Hiroshi Abe, Tomoaki Nishimura.:
  "Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network"
  The 8th International Conference on Learning Representations (ICLR 2020)

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