Hugging Face Trending Papers August 17, 2026

Operator-Theoretic Generalization Bounds for Multitask Deep Learning

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We develop operator-theoretic generalization bounds for deep multi-output function classes by representing network layers as Koopman composition operators on vector-valued reproducing kernel Hilbert spaces. In vector-valued Sobolev RKHSs, we derive Rademacher complexity bounds for invertible and width-expanding injective architectures.

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arXiv Machine Learning

1d ago

Operator-Theoretic Generalization Bounds for Multitask Deep Learning

arXiv:2608. 15982v1 Announce Type: new Abstract: We develop operator-theoretic generalization bounds for deep multi-output function classes by representing network layers as Koopman composition operators on vector-valued reproducing kernel Hilbert spaces.

By Mahdi Mohammadigohari, Thomas Borsani, Giuseppe Di Fatta

arXiv Machine Learning

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arXiv Machine Learning

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