Derives tighter Koopman-based generalization bounds for multi-task DNNs by assuming small weight-matrix condition numbers and using a tailored Sobolev space as hypothesis class.
Journal of Machine Learning Research25(181), 1–51 (2024),http: //jmlr.org/papers/v25/23-1663.html
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Derives tighter generalization bounds for vector-valued neural networks and deep kernel methods in multi-task learning via Koopman and PF operators, with sketching for efficiency and a new vvRKHS framework.
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On the Koopman-Based Generalization Bounds for Multi-Task Deep Learning
Derives tighter Koopman-based generalization bounds for multi-task DNNs by assuming small weight-matrix condition numbers and using a tailored Sobolev space as hypothesis class.
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Operator-Based Generalization Bound for Deep Learning: Insights on Multi-Task Learning
Derives tighter generalization bounds for vector-valued neural networks and deep kernel methods in multi-task learning via Koopman and PF operators, with sketching for efficiency and a new vvRKHS framework.