Approximation beats concentration? An approximation view on inference with smooth radial kernels

10 January 2018

Papers citing "Approximation beats concentration? An approximation view on inference with smooth radial kernels"

12 / 12 papers shown

Title
Stochastic Nonlinear Control via Finite-dimensional Spectral Dynamic Embedding Tongzheng Ren Zhaolin Ren Haitong Ma Na Li Bo Dai 30 10 0 08 Apr 2023
Interpolation with the polynomial kernels G. Elefante W. Erb Francesco Marchetti E. Perracchione D. Poggiali G. Santin 19 1 0 15 Dec 2022
Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models Qiong Wu Jian Li Zhenming Liu Yanhua Li Mihai Cucuringu 31 4 0 01 Dec 2022
Provably Reliable Large-Scale Sampling from Gaussian Processes Anthony Stephenson Robert Allison Edward O. Pyzer-Knapp 16 2 0 15 Nov 2022
A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks E. M. Achour Armand Foucault Sébastien Gerchinovitz Franccois Malgouyres 32 7 0 09 Jun 2022
Rigorous data-driven computation of spectral properties of Koopman operators for dynamical systems Matthew J. Colbrook Alex Townsend 18 67 0 29 Nov 2021
Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces George Stepaniants 49 15 0 26 Aug 2021
Adversarially Robust Kernel Smoothing Jia-Jie Zhu Christina Kouridi Yassine Nemmour Bernhard Schölkopf 23 7 0 16 Feb 2021
On Information Gain and Regret Bounds in Gaussian Process Bandits Sattar Vakili Kia Khezeli Victor Picheny GP 23 126 0 15 Sep 2020
Interpolation and Learning with Scale Dependent Kernels Nicolò Pagliana Alessandro Rudi E. De Vito Lorenzo Rosasco 36 8 0 17 Jun 2020
Scalable Thompson Sampling using Sparse Gaussian Process Models Sattar Vakili Henry B. Moss A. Artemev Vincent Dutordoir Victor Picheny 8 34 0 09 Jun 2020
Towards Understanding the Spectral Bias of Deep Learning Yuan Cao Zhiying Fang Yue Wu Ding-Xuan Zhou Quanquan Gu 32 214 0 03 Dec 2019