Establishes near-optimal dimension-independent convergence rates for regularized SGD with operator-valued kernels in statistical inverse problems for operator learning.
Sous-espaces hilbertiens d’espaces vectoriels topologiques et noyaux associ´ es (noyaux reproduisants).Journal D’analyse Math´ ematique, 13:115–256
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Learning Operators by Regularized Stochastic Gradient Descent with Operator-valued Kernels
Establishes near-optimal dimension-independent convergence rates for regularized SGD with operator-valued kernels in statistical inverse problems for operator learning.