Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators. - Isao Ishikawa; Keisuke Fujii 001; Masahiro Ikeda; Yuka Hashimoto; Yoshinobu Kawahara (NeurIPS, 2018)

Metric on Nonlinear Dynamical Systems with Koopman Operators. - Isao Ishikawa; Keisuke Fujii 001; Masahiro Ikeda; Yuka Hashimoto; Yoshinobu Kawahara (CoRR, 2018)

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)

Kernel Mean Embeddings of Von Neumann-Algebra-Valued Measures. - Yuka Hashimoto; Isao Ishikawa; Masahiro Ikeda; Fuyuta Komura; Yoshinobu Kawahara (CoRR, 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)

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)

C*-algebra Net: A New Approach Generalizing Neural Network Parameters to C*-algebra. - Yuka Hashimoto; Zhao Wang 009; Tomoko Matsui (ICML, 2022)

C*-algebra Net: A New Approach Generalizing Neural Network Parameters to C*-algebra. - Yuka Hashimoto; Zhao Wang 009; Tomoko Matsui (CoRR, 2022)

Learning in RKHM: a C*-Algebraic Twist for Kernel Machines. - Yuka Hashimoto; Masahiro Ikeda; Hachem Kadri (CoRR, 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 (AISTATS, 2023)

Deep learning with kernels through RKHM and the Perron-Frobenius operator. - Yuka Hashimoto; Masahiro Ikeda; Hachem Kadri (NeurIPS, 2023)

Noncommutative C*-algebra Net: Learning Neural Networks with Powerful Product Structure in C*-algebra. - Ryuichiro Hataya; Yuka Hashimoto (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)

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 generalization bound: New aspect for full-rank weights. - Yuka Hashimoto; Sho Sonoda; Isao Ishikawa; Atsushi Nitanda; Taiji Suzuki (ICLR, 2024)

Position: C∗-Algebraic Machine Learning - Moving in a New Direction. - Yuka Hashimoto; Masahiro Ikeda; Hachem Kadri (ICML, 2024)

C*-Algebraic Machine Learning: Moving in a New Direction. - Yuka Hashimoto; Masahiro Ikeda; Hachem Kadri (CoRR, 2024)

Koopman operators with intrinsic observables in rigged reproducing kernel Hilbert spaces. - Isao Ishikawa; Yuka Hashimoto; Masahiro Ikeda; Yoshinobu Kawahara (CoRR, 2024)

Quantum Circuit C*-algebra Net. - Yuka Hashimoto; Ryuichiro Hataya (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)

Spectral Truncation Kernels: Noncommutativity in C*-algebraic Kernel Machines. - Yuka Hashimoto; Ayoub Hafid; Masahiro Ikeda; Hachem Kadri (CoRR, 2024)

Deep Koopman-layered Model with Universal Property Based on Toeplitz Matrices. - Yuka Hashimoto; Tomoharu Iwata (CoRR, 2024)

Generalization Through Growth: Hidden Dynamics Controls Depth Dependence. - Sho Sonoda; Yuka Hashimoto; Isao Ishikawa; Masahiro Ikeda (CoRR, 2025)

Towards Quantum Operator-Valued Kernels. - Hachem Kadri; Joachim Tomasi; Yuka Hashimoto; Sandrine Anthoine (CoRR, 2025)