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Minimax-optimal semi-supervised regression on unknown manifolds
v1v2 (latest)

Minimax-optimal semi-supervised regression on unknown manifolds

7 November 2016
Amit Moscovich
Ariel Jaffe
B. Nadler
ArXiv (abs)PDFHTML

Papers citing "Minimax-optimal semi-supervised regression on unknown manifolds"

18 / 18 papers shown
Diffusion Models and the Manifold Hypothesis: Log-Domain Smoothing is Geometry Adaptive
Diffusion Models and the Manifold Hypothesis: Log-Domain Smoothing is Geometry Adaptive
Tyler Farghly
Peter Potaptchik
Samuel Howard
George Deligiannidis
Jakiw Pidstrigach
DiffM
228
3
0
02 Oct 2025
Learning Distances from Data with Normalizing Flows and Score Matching
Learning Distances from Data with Normalizing Flows and Score Matching
Peter Sorrenson
Daniel Behrend-Uriarte
Christoph Schnörr
Ullrich Kothe
308
8
0
12 Jul 2024
Semi-supervised Fréchet Regression
Semi-supervised Fréchet Regression
Rui Qiu
Zhou Yu
Zhenhua Lin
194
0
0
16 Apr 2024
Fermat Distances: Metric Approximation, Spectral Convergence, and
  Clustering Algorithms
Fermat Distances: Metric Approximation, Spectral Convergence, and Clustering Algorithms
Nicolas García Trillos
A. Little
Daniel McKenzie
James M. Murphy
283
10
0
07 Jul 2023
Diffusion Maps for Group-Invariant Manifolds
Diffusion Maps for Group-Invariant Manifolds
Paulina Hoyos
Joe Kileel
212
2
0
28 Mar 2023
Deep Clustering with a Constraint for Topological Invariance based on
  Symmetric InfoNCE
Deep Clustering with a Constraint for Topological Invariance based on Symmetric InfoNCENeural Computation (Neural Comput.), 2023
Yuhui Zhang
Yuichiro Wada
Hiroki Waida
Kaito Goto
Yusaku Hino
Takafumi Kanamori
240
8
0
06 Mar 2023
Built Year Prediction from Buddha Face with Heterogeneous Labels
Built Year Prediction from Buddha Face with Heterogeneous Labels
Yiming Qian
Cheikh Brahim El Vaigh
Yuta Nakashima
B. Renoust
Hajime Nagahara
Yutaka Fujioka
CVBM
262
4
0
02 Sep 2021
Manifold learning with arbitrary norms
Manifold learning with arbitrary normsJournal of Fourier Analysis and Applications (JFAA), 2020
Joe Kileel
Amit Moscovich
Nathan Zelesko
A. Singer
436
29
0
28 Dec 2020
Approximating the Riemannian Metric from Point Clouds via Manifold
  Moving Least Squares
Approximating the Riemannian Metric from Point Clouds via Manifold Moving Least Squares
B. Sober
Robert J. Ravier
Ingrid Daubechies
319
8
0
20 Jul 2020
Earthmover-based manifold learning for analyzing molecular conformation
  spaces
Earthmover-based manifold learning for analyzing molecular conformation spacesIEEE International Symposium on Biomedical Imaging (ISBI), 2019
Nathan Zelesko
Amit Moscovich
Joe Kileel
A. Singer
DiffM
239
20
0
16 Oct 2019
Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral
  volumes
Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumesInverse Problems (IP), 2019
Amit Moscovich
Amit Halevi
Joakim Andén
A. Singer
242
59
0
01 Jul 2019
Power Weighted Shortest Paths for Clustering Euclidean Data
Power Weighted Shortest Paths for Clustering Euclidean DataFoundations of Data Science (FODS), 2019
Daniel McKenzie
S. Damelin
246
20
0
30 May 2019
High-dimensional semi-supervised learning: in search for optimal
  inference of the mean
High-dimensional semi-supervised learning: in search for optimal inference of the mean
Yuqian Zhang
Jelena Bradic
144
34
0
02 Feb 2019
Manifold regularization with GANs for semi-supervised learning
Manifold regularization with GANs for semi-supervised learning
Bruno Lecouat
Chuan-Sheng Foo
Houssam Zenati
V. Chandrasekhar
GAN
110
14
0
11 Jul 2018
Minimax rates for cost-sensitive learning on manifolds with approximate
  nearest neighbours
Minimax rates for cost-sensitive learning on manifolds with approximate nearest neighboursInternational Conference on Algorithmic Learning Theory (ALT), 2017
Henry W. J. Reeve
Gavin Brown
186
11
0
01 Mar 2018
Achieving the time of $1$-NN, but the accuracy of $k$-NN
Achieving the time of 111-NN, but the accuracy of kkk-NN
Lirong Xue
Samory Kpotufe
188
14
0
06 Dec 2017
A random matrix analysis and improvement of semi-supervised learning for
  large dimensional data
A random matrix analysis and improvement of semi-supervised learning for large dimensional data
Xiaoyi Mai
Romain Couillet
296
44
0
09 Nov 2017
Approximation of Functions over Manifolds: A Moving Least-Squares
  Approach
Approximation of Functions over Manifolds: A Moving Least-Squares Approach
B. Sober
Yariv Aizenbud
D. Levin
394
24
0
02 Nov 2017
1
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