Yuka Hashimoto

According to our database, Yuka Hashimoto authored at least 29 papers between 2018 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Unified generalization analysis for physics informed neural networks.

Yuka Hashimoto, Tomoharu Iwata

CoRR, May, 2026

Deep Koopman-layered model with universal property based on toeplitz matrices.

Yuka Hashimoto, Tomoharu Iwata

Neurocomputing, 2026

Frequency-Informed Encoder-Decoder Models with Expressivity for Time Series Anomaly Detection.

Daichi Fushihara, Yuka Hashimoto, Yoichi Matsuo

Proceedings of the 23rd Consumer Communications & Networking Conference, 2026

2025

Why High-rank Neural Networks Generalize?: An Algebraic Framework with RKHSs.

Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Masahiro Ikeda

CoRR, September, 2025

Towards Quantum Operator-Valued Kernels.

Hachem Kadri, Joachim Tomasi, Yuka Hashimoto, Sandrine Anthoine

CoRR, June, 2025

Generalization Through Growth: Hidden Dynamics Controls Depth Dependence.

Sho Sonoda, Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda

CoRR, May, 2025

Deep Ridgelet Transform and Unified Universality Theorem for Deep and Shallow Joint-Group-Equivariant Machines.

Sho Sonoda, Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Spectral Truncation Kernels: Noncommutativity in C^*-algebraic Kernel Machines.

Yuka Hashimoto, Ayoub Hafid, Masahiro Ikeda, Hachem Kadri

CoRR, 2024

Constructive Universal Approximation Theorems for Deep Joint-Equivariant Networks by Schur's Lemma.

Sho Sonoda, Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda

CoRR, 2024

Quantum Circuit C^*-algebra Net.

Yuka Hashimoto, Ryuichiro Hataya

CoRR, 2024

Koopman operators with intrinsic observables in rigged reproducing kernel Hilbert spaces.

Isao Ishikawa, Yuka Hashimoto, Masahiro Ikeda, Yoshinobu Kawahara

CoRR, 2024

C^*-Algebraic Machine Learning: Moving in a New Direction.

Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri

CoRR, 2024

Position: C∗-Algebraic Machine Learning - Moving in a New Direction.

Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Koopman-based generalization bound: New aspect for full-rank weights.

Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Atsushi Nitanda, Taiji Suzuki

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Deep Ridgelet Transform: Voice with Koopman Operator Proves Universality of Formal Deep Networks.

Sho Sonoda, Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda

CoRR, 2023

Koopman-Based Bound for Generalization: New Aspect of Neural Networks Regarding Nonlinear Noise Filtering.

Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Atsushi Nitanda, Taiji Suzuki

CoRR, 2023

Noncommutative C^*-algebra Net: Learning Neural Networks with Powerful Product Structure in C^*-algebra.

Ryuichiro Hataya, Yuka Hashimoto

CoRR, 2023

Deep learning with kernels through RKHM and the Perron-Frobenius operator.

Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning in RKHM: a C*-Algebraic Twist for Kernel Machines.

Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

A preconditioning technique for Krylov subspace methods in RKHSs.

Yuka Hashimoto, Takashi Nodera

J. Comput. Appl. Math., 2022

Learning in RKHM: a C^*-Algebraic Twist for Kernel Machines.

Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri

CoRR, 2022

C^*-algebra Net: A New Approach Generalizing Neural Network Parameters to C^*-algebra.

Yuka Hashimoto, Zhao Wang, Tomoko Matsui

CoRR, 2022

C*-algebra Net: A New Approach Generalizing Neural Network Parameters to C*-algebra.

Yuka Hashimoto, Zhao Wang, Tomoko Matsui

Proceedings of the International Conference on Machine Learning, 2022

2021

Reproducing kernel Hilbert C*-module and kernel mean embeddings.

Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Takeshi Katsura, Yoshinobu Kawahara

J. Mach. Learn. Res., 2021

2020

Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise.

Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Yoichi Matsuo, Yoshinobu Kawahara

J. Mach. Learn. Res., 2020

Kernel Mean Embeddings of Von Neumann-Algebra-Valued Measures.

Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Yoshinobu Kawahara

CoRR, 2020

Analysis via Orthonormal Systems in Reproducing Kernel Hilbert C^*-Modules and Applications.

Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Takeshi Katsura, Yoshinobu Kawahara

CoRR, 2020

2018

Metric on Nonlinear Dynamical Systems with Koopman Operators.

Isao Ishikawa, Keisuke Fujii, Masahiro Ikeda, Yuka Hashimoto, Yoshinobu Kawahara

CoRR, 2018

Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators.

Isao Ishikawa, Keisuke Fujii, Masahiro Ikeda, Yuka Hashimoto, Yoshinobu Kawahara

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018