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cs.LG2025
Zero Generalization Error Theorem for Random Interpolators via Algebraic Geometry
Naoki Yoshida, Isao Ishikawa, Masaaki Imaizumi
We theoretically demonstrate that the generalization error of interpolators for machine learning models under teacher-student settings becomes 0 once the number of training samples…
cs.LG2025
Generative Modeling through Koopman Spectral Analysis: An Operator-Theoretic Perspective
Yuanchao Xu, Fengyi Li, Masahiro Fujisawa +3
We propose Koopman Spectral Wasserstein Gradient Descent (KSWGD), a particle-based generative modeling framework that learns the Langevin generator via Koopman theory and integrate…