A Kernel-based Stochastic Approximation Framework for Nonlinear Operator Learning
stat.ML, cs.LG, cs.NA, math.FA, math.NA, math.ST, stat.TH
Submitted: 2025-09-14
Updated: 2026-09-27
Code: https://github.com/JiaqiYang-Fdu/Stochastic-Approximation-Operator-Learning
Terminology
Sources
- Structured Prediction in Online Learning
- Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis
- Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms
- Learning linear operators: Infinite-dimensional regression as a well-behaved non-compact inverse problem
- Optimal Convergence Rates for Neural Operators
- Stochastic Gradient Descent for Operator Learning in Hilbert Spaces: Convergence Rates and Minimax Lower Bounds
- Operator Learning: A Statistical Perspective
- Learning Operators by Regularized Stochastic Gradient Descent with Operator-valued Kernels
- Neural Operators for Nonlinear Functionals on RKHS
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