Error Bounds for Learning with Vector-Valued Random Features

26 May 2023

Papers citing "Error Bounds for Learning with Vector-Valued Random Features"

11 / 11 papers shown

Title
Joker: Joint Optimization Framework for Lightweight Kernel Machines Junhong Zhang Zhihui Lai 45 0 0 23 May 2025
Hyperparameter Optimization for Randomized Algorithms: A Case Study on Random Features Oliver R. A. Dunbar Nicholas H. Nelsen Maya Mutic 113 7 0 30 Jun 2024
Minimax Optimal Kernel Operator Learning via Multilevel Training Jikai Jin Yiping Lu Jose H. Blanchet Lexing Ying 78 13 0 28 Sep 2022
Concentration of Random Feature Matrices in High-Dimensions Zhijun Chen Hayden Schaeffer Rachel A. Ward 68 6 0 14 Apr 2022
Convergence Rates for Learning Linear Operators from Noisy Data Maarten V. de Hoop Nikola B. Kovachki Nicholas H. Nelsen Andrew M. Stuart 161 57 0 27 Aug 2021
Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces George Stepaniants 62 19 0 26 Aug 2021
Generalization error of random features and kernel methods: hypercontractivity and kernel matrix concentration Song Mei Theodor Misiakiewicz Andrea Montanari 81 111 0 26 Jan 2021
DeepGreen: Deep Learning of Green's Functions for Nonlinear Boundary Value Problems Craig Gin D. Shea Steven L. Brunton J. Nathan Kutz 52 89 0 31 Dec 2020
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 494 2,401 0 18 Oct 2020
Approximation Bounds for Random Neural Networks and Reservoir Systems Lukas Gonon Lyudmila Grigoryeva Juan-Pablo Ortega 77 67 0 14 Feb 2020
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 229 2,123 0 08 Oct 2019