Establishes near-optimal dimension-independent convergence rates for regularized SGD with operator-valued kernels in statistical inverse problems for operator learning.
Optimal rates for the regularized least-squares algo- rithm.Foundations of Computational Mathematics, 7:331–368
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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.