Establishes near-optimal dimension-independent convergence rates for regularized SGD with operator-valued kernels in statistical inverse problems for operator learning.
Online learning as stochastic approximation of regularization paths: Optimality and almost-sure convergence.IEEE Transactions on Information Theory, 60(9):5716– 5735, 2014
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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.