Papers
Communities
Events
Blog
Pricing
Search
Open menu
Home
Papers
2211.08875
Cited By
Learning linear operators: Infinite-dimensional regression as a well-behaved non-compact inverse problem
16 November 2022
Mattes Mollenhauer
Nicole Mücke
T. Sullivan
Re-assign community
ArXiv
PDF
HTML
Papers citing
"Learning linear operators: Infinite-dimensional regression as a well-behaved non-compact inverse problem"
23 / 23 papers shown
Title
Kernel-Based Optimal Control: An Infinitesimal Generator Approach
Petar Bevanda
Nicolas Hosichen
Tobias Wittmann
Jan Brüdigam
Sandra Hirche
Boris Houska
68
0
0
02 Dec 2024
Operator Learning Using Random Features: A Tool for Scientific Computing
Nicholas H. Nelsen
Andrew M. Stuart
35
12
0
12 Aug 2024
Optimal Estimation of Structured Covariance Operators
Omar Al Ghattas
Jiaheng Chen
D. Sanz-Alonso
Nathan Waniorek
29
3
0
04 Aug 2024
Optimal Rates for Functional Linear Regression with General Regularization
Naveen Gupta
S. Sivananthan
Bharath K. Sriperumbudur
21
4
0
14 Jun 2024
Covariance Operator Estimation via Adaptive Thresholding
Omar Al Ghattas
D. Sanz-Alonso
30
1
0
28 May 2024
Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms
D. Meunier
Zikai Shen
Mattes Mollenhauer
Arthur Gretton
Zhu Li
32
4
0
23 May 2024
Nonparametric Control Koopman Operators
Petar Bevanda
Bas Driessen
Lucian-Cristian Iacob
Roland Toth
Stefan Sosnowski
Sandra Hirche
28
2
0
12 May 2024
Operator Learning: Algorithms and Analysis
Nikola B. Kovachki
S. Lanthaler
Andrew M. Stuart
36
22
0
24 Feb 2024
Optimal linear prediction with functional observations: Why you can use a simple post-dimension reduction estimator
Won-Ki Seo
16
1
0
12 Jan 2024
A randomized algorithm to solve reduced rank operator regression
G. Turri
Vladimir Kostic
P. Novelli
Massimiliano Pontil
27
4
0
28 Dec 2023
Learned reconstruction methods for inverse problems: sample error estimates
Luca Ratti
24
0
0
21 Dec 2023
Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm
Zhu Li
D. Meunier
Mattes Mollenhauer
Arthur Gretton
29
5
0
12 Dec 2023
Inverse Problems with Learned Forward Operators
Simon Arridge
Andreas Hauptmann
Yury Korolev
26
1
0
21 Nov 2023
On regularized polynomial functional regression
Markus Holzleitner
S. Pereverzyev
19
6
0
06 Nov 2023
Covariance Operator Estimation: Sparsity, Lengthscale, and Ensemble Kalman Filters
Omar Al Ghattas
Jiaheng Chen
D. Sanz-Alonso
Nathan Waniorek
19
4
0
25 Oct 2023
Online Infinite-Dimensional Regression: Learning Linear Operators
Vinod Raman
Unique Subedi
Ambuj Tewari
27
0
0
08 Sep 2023
Error Bounds for Learning with Vector-Valued Random Features
S. Lanthaler
Nicholas H. Nelsen
27
12
0
26 May 2023
Linear estimators for Gaussian random variables in Hilbert spaces
Stefan Tappe
12
0
0
18 May 2023
Machine Learning for Partial Differential Equations
Steven L. Brunton
J. Nathan Kutz
AI4CE
24
20
0
30 Mar 2023
Domain Generalization by Functional Regression
Markus Holzleitner
S. Pereverzyev
Werner Zellinger
OOD
21
4
0
09 Feb 2023
Nonparametric approximation of conditional expectation operators
Mattes Mollenhauer
P. Koltai
24
17
0
23 Dec 2020
A note on estimation in Hilbertian linear models
Siegfried Hormann
Łukasz Kidziński
66
34
0
14 Aug 2012
Conditional mean embeddings as regressors - supplementary
Steffen Grunewalder
Guy Lever
Luca Baldassarre
Sam Patterson
A. Gretton
Massimiliano Pontil
80
143
0
21 May 2012
1