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Functional Analysis

23 directly classified papers

Papers per year

Papers

Towards Unified Native Spaces in Kernel Methods JMLR 2025
Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces JMLR 2024
A Data-Adaptive RKHS Prior for Bayesian Learning of Kernels in Operators JMLR 2024
Optimal deep learning of holomorphic operators between Banach spaces NIPS 2024
Inverse M-Kernels for Linear Universal Approximators of Non-Negative Functions NIPS 2024
Geometric Learning with Positively Decomposable Kernels JMLR 2024
Gradient Descent in Neural Networks as Sequential Learning in Reproducing Kernel Banach Space ICML 2023
Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective ICML 2023
Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces JMLR 2023
Kernel interpolation in Sobolev spaces is not consistent in low dimensions COLT 2022
Sobolev Norm Learning Rates for Conditional Mean Embeddings AISTATS 2022
A Data Driven, Convex Optimization Approach to Learning Koopman Operators L4DC 2021
On Universal Approximation and Error Bounds for Fourier Neural Operators JMLR 2021
Ivanov-Regularised Least-Squares Estimators over Large RKHSs and Their Interpolation Spaces JMLR 2019
Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators NIPS 2018
On denoising modulo 1 samples of a function AISTATS 2018
Dissipativity Theory for Nesterov’s Accelerated Method ICML 2017
Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces NIPS 2014
Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space NIPS 2013
Smooth Operators ICML 2013
The representer theorem for Hilbert spaces: a necessary and sufficient condition NIPS 2012
Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint NIPS 2011
Frames, Reproducing Kernels, Regularization and Learning JMLR 2005