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Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion
  spaces

Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces

Analysis and Applications (Anal. Appl.), 2019
27 May 2019
Ernesto De Vito
Nicole Mücke
Lorenzo Rosasco
ArXiv (abs)PDFHTML

Papers citing "Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces"

22 / 22 papers shown
Expressive Power of Deep Networks on Manifolds: Simultaneous Approximation
Expressive Power of Deep Networks on Manifolds: Simultaneous Approximation
Hanfei Zhou
Lei Shi
246
0
0
11 Sep 2025
Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes
Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes
Sidhanth Holalkere
David S. Bindel
Silvia Sellán
Alexander Terenin
486
2
0
24 Mar 2025
Residual Deep Gaussian Processes on Manifolds
Residual Deep Gaussian Processes on ManifoldsInternational Conference on Learning Representations (ICLR), 2024
Kacper Wyrwal
Andreas Krause
Viacheslav Borovitskiy
BDL
305
3
0
31 Oct 2024
Diffusion-based Semi-supervised Spectral Algorithm for Regression on
  Manifolds
Diffusion-based Semi-supervised Spectral Algorithm for Regression on Manifolds
Weichun Xia
Jiaxin Jiang
Lei Shi
217
0
0
18 Oct 2024
Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces
Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces
Nicolas Hoischen
Nicolas Hoischen
Roland Toth
Sandra Hirche
Boris Houska
371
2
0
23 Jul 2024
Spectral Algorithms on Manifolds through Diffusion
Spectral Algorithms on Manifolds through Diffusion
Weichun Xia
Lei Shi
374
1
0
06 Mar 2024
Implicit Manifold Gaussian Process Regression
Implicit Manifold Gaussian Process RegressionNeural Information Processing Systems (NeurIPS), 2023
Bernardo Fichera
Viacheslav Borovitskiy
Andreas Krause
A. Billard
271
8
0
30 Oct 2023
Intrinsic Gaussian Vector Fields on Manifolds
Intrinsic Gaussian Vector Fields on ManifoldsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Daniel Robert-Nicoud
Andreas Krause
Viacheslav Borovitskiy
361
8
0
28 Oct 2023
Posterior Contraction Rates for Matérn Gaussian Processes on
  Riemannian Manifolds
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian ManifoldsNeural Information Processing Systems (NeurIPS), 2023
Paul Rosa
Viacheslav Borovitskiy
Alexander Terenin
Judith Rousseau
423
14
0
19 Sep 2023
Central Limit Theorems and Approximation Theory: Part II
Central Limit Theorems and Approximation Theory: Part II
Arun K. Kuchibhotla
151
0
0
26 Jun 2023
Stationary Kernels and Gaussian Processes on Lie Groups and their
  Homogeneous Spaces II: non-compact symmetric spaces
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces
I. Azangulov
A. Smolensky
Alexander Terenin
Viacheslav Borovitskiy
365
17
0
30 Jan 2023
Strictly positive definite kernels on compact Riemannian manifolds
Strictly positive definite kernels on compact Riemannian manifolds
J. Guella
Janin Jäger
94
0
0
19 Jan 2023
Spectral Representation Learning for Conditional Moment Models
Spectral Representation Learning for Conditional Moment Models
Ziyu Wang
Yucen Luo
Yueru Li
Chao Ding
Bernhard Schölkopf
CML
354
13
0
29 Oct 2022
A Framework for Improving the Characterization Scope of Stein's Method
  on Riemannian Manifolds
A Framework for Improving the Characterization Scope of Stein's Method on Riemannian Manifolds
Xiaoda Qu
B. Vemuri
129
0
0
17 Sep 2022
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case
I. Azangulov
A. Smolensky
Alexander Terenin
Viacheslav Borovitskiy
438
27
0
31 Aug 2022
On the speed of uniform convergence in Mercer's theorem
On the speed of uniform convergence in Mercer's theoremJournal of Mathematical Analysis and Applications (JMAA), 2022
Rustem Takhanov
167
9
0
01 May 2022
Geometry-aware Bayesian Optimization in Robotics using Riemannian
  Matérn Kernels
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn KernelsConference on Robot Learning (CoRL), 2021
Noémie Jaquier
Viacheslav Borovitskiy
A. Smolensky
Alexander Terenin
Tamim Asfour
Leonel Rozo
460
42
0
02 Nov 2021
Stochastic Gradient Descent in Hilbert Scales: Smoothness,
  Preconditioning and Earlier Stopping
Stochastic Gradient Descent in Hilbert Scales: Smoothness, Preconditioning and Earlier Stopping
Nicole Mücke
Enrico Reiss
160
7
0
18 Jun 2020
Matérn Gaussian processes on Riemannian manifolds
Matérn Gaussian processes on Riemannian manifolds
Viacheslav Borovitskiy
Alexander Terenin
P. Mostowsky
M. Deisenroth
693
148
0
17 Jun 2020
Stabilizing Training of Generative Adversarial Nets via Langevin Stein
  Variational Gradient Descent
Stabilizing Training of Generative Adversarial Nets via Langevin Stein Variational Gradient Descent
Dong Wang
Xiaoqian Qin
F. Song
Li Cheng
GAN
244
27
0
22 Apr 2020
How Well Generative Adversarial Networks Learn Distributions
How Well Generative Adversarial Networks Learn DistributionsJournal of machine learning research (JMLR), 2018
Tengyuan Liang
GAN
377
116
0
07 Nov 2018
A Riemann-Stein Kernel Method
A Riemann-Stein Kernel Method
Alessandro Barp
Christine J. Oates
Emilio Porcu
Mark Girolami
420
26
0
11 Oct 2018
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