$Spectral Convergence of Graph Laplacian and Heat Kernel Reconstruction in $L^\infty$ from Random Samples$

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Spectral Convergence of Graph Laplacian and Heat Kernel Reconstruction in $L^\infty$ from Random Samples

11 December 2019

Papers citing "Spectral Convergence of Graph Laplacian and Heat Kernel Reconstruction in $L^\infty$ from Random Samples"

49 / 49 papers shown

Title
Convergence of Manifold Filter-Combine Networks David R. Johnson Joyce A. Chew Siddharth Viswanath E. Brouwer Deanna Needell Smita Krishnaswamy Michael Perlmutter 3DPC 71 0 0 18 Oct 2024
Diffusion-based Semi-supervised Spectral Algorithm for Regression on Manifolds Weichun Xia Jiaxin Jiang Lei Shi 44 0 0 18 Oct 2024
Spectral Self-supervised Feature Selection Daniel Segal Ofir Lindenbaum Ariel Jaffe 103 0 0 12 Jul 2024
Temporal label recovery from noisy dynamical data Y. Khoo Xin T. Tong Wanjie Wang Yuguan Wang 81 2 0 19 Jun 2024
A Manifold Perspective on the Statistical Generalization of Graph Neural Networks Zhiyang Wang J. Cerviño Alejandro Ribeiro AI4CE GNN 74 9 0 07 Jun 2024
Nonparametric regression on random geometric graphs sampled from submanifolds Paul Rosa Judith Rousseau 126 1 0 31 May 2024
Scalable Bayesian inference for heat kernel Gaussian processes on manifolds Junhui He Guoxuan Ma Jian Kang Ying Yang 50 0 0 22 May 2024
Nonsmooth Nonparametric Regression via Fractional Laplacian Eigenmaps Zhaoyang Shi Krishnakumar Balasubramanian W. Polonik 81 1 0 22 Feb 2024
Adaptive and non-adaptive minimax rates for weighted Laplacian-eigenmap based nonparametric regression Zhaoyang Shi Krishnakumar Balasubramanian W. Polonik 65 2 0 31 Oct 2023
Implicit Manifold Gaussian Process Regression Bernardo Fichera Viacheslav Borovitskiy Andreas Krause A. Billard 50 4 0 30 Oct 2023
Spectral Neural Networks: Approximation Theory and Optimization Landscape Chenghui Li Rishi Sonthalia Nicolas García Trillos 83 1 0 01 Oct 2023
Continuum Limits of Ollivier's Ricci Curvature on data clouds: pointwise consistency and global lower bounds Nicolas García Trillos Melanie Weber 129 4 0 05 Jul 2023
Representing and Learning Functions Invariant Under Crystallographic Groups Ryan P. Adams Peter Orbanz 116 4 0 08 Jun 2023
Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs Zhiyang Wang Luana Ruiz Alejandro Ribeiro GNN 72 7 0 29 May 2023
Tangent Bundle Convolutional Learning: from Manifolds to Cellular Sheaves and Back Claudio Battiloro Zhiyang Wang Hans Riess Paolo Di Lorenzo Alejandro Ribeiro 72 11 0 20 Mar 2023
A Convergence Rate for Manifold Neural Networks Joyce A. Chew Deanna Needell Michael Perlmutter 70 6 0 23 Dec 2022
Convolutional Filtering on Sampled Manifolds Zhiyang Wang Luana Ruiz Alejandro Ribeiro 65 3 0 20 Nov 2022
Augmentation Invariant Manifold Learning Shulei Wang 244 1 0 01 Nov 2022
Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis Ce Ju Cuntai Guan 107 22 0 25 Oct 2022
Convolutional Neural Networks on Manifolds: From Graphs and Back Zhiyang Wang Luana Ruiz Alejandro Ribeiro 3DPC GNN 94 14 0 01 Oct 2022
Learning Globally Smooth Functions on Manifolds J. Cerviño Luiz F. O. Chamon B. Haeffele René Vidal Alejandro Ribeiro 99 6 0 01 Oct 2022
Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling Boris Landa Xiuyuan Cheng 101 6 0 16 Sep 2022
Large data limit of the MBO scheme for data clustering: convergence of the dynamics Tim Laux Jona Lelmi 59 8 0 13 Sep 2022
Geometric Scattering on Measure Spaces Joyce A. Chew M. Hirn Smita Krishnaswamy Deanna Needell Michael Perlmutter H. Steach Siddharth Viswanath Hau‐Tieng Wu GNN 230 19 0 17 Aug 2022
Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise Xiuyuan Cheng Boris Landa 62 3 0 22 Jun 2022
The Manifold Scattering Transform for High-Dimensional Point Cloud Data Joyce A. Chew H. Steach Siddharth Viswanath Hau‐Tieng Wu M. Hirn Deanna Needell Smita Krishnaswamy Michael Perlmutter 3DPC 71 13 0 21 Jun 2022
SpecNet2: Orthogonalization-free spectral embedding by neural networks Ziyu Chen Yingzhou Li Xiuyuan Cheng 54 4 0 14 Jun 2022
Learning Low-Dimensional Nonlinear Structures from High-Dimensional Noisy Data: An Integral Operator Approach Xiucai Ding Rongkai Ma 95 9 0 28 Feb 2022
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets Tal Shnitzer Mikhail Yurochkin Kristjan Greenewald Justin Solomon 90 6 0 03 Feb 2022
Spatiotemporal Analysis Using Riemannian Composition of Diffusion Operators Tal Shnitzer Hau‐Tieng Wu Ronen Talmon 39 10 0 21 Jan 2022
Minimax Optimal Regression over Sobolev Spaces via Laplacian Eigenmaps on Neighborhood Graphs Alden Green Sivaraman Balakrishnan Robert Tibshirani 117 12 0 14 Nov 2021
Topologically penalized regression on manifolds Olympio Hacquard Krishnakumar Balasubramanian Gilles Blanchard Clément Levrard W. Polonik 95 4 0 26 Oct 2021
Inferring Manifolds From Noisy Data Using Gaussian Processes David B. Dunson Nan Wu 91 18 0 14 Oct 2021
Clustering dynamics on graphs: from spectral clustering to mean shift through Fokker-Planck interpolation Katy Craig Nicolas García Trillos D. Slepčev 42 6 0 18 Aug 2021
Large sample spectral analysis of graph-based multi-manifold clustering Nicolas García Trillos Pengfei He Chenghui Li 144 6 0 28 Jul 2021
Non-Parametric Manifold Learning D. Asta 19 0 0 16 Jul 2021
Solving PDEs on Unknown Manifolds with Machine Learning Senwei Liang Shixiao W. Jiang J. Harlim Haizhao Yang AI4CE 114 16 0 12 Jun 2021
Stability to Deformations of Manifold Filters and Manifold Neural Networks Zhiyang Wang Luana Ruiz Alejandro Ribeiro AAML 50 9 0 07 Jun 2021
Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey Benyamin Ghojogh A. Ghodsi Fakhri Karray Mark Crowley 65 12 0 03 Jun 2021
Kernel Two-Sample Tests for Manifold Data Xiuyuan Cheng Yao Xie 43 9 0 07 May 2021
Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation Xiuyuan Cheng Nan Wu 122 29 0 25 Jan 2021
Impact of signal-to-noise ratio and bandwidth on graph Laplacian spectrum from high-dimensional noisy point cloud Xiucai Ding Hau‐Tieng Wu 119 13 0 21 Nov 2020
Convergence of Graph Laplacian with kNN Self-tuned Kernels Xiuyuan Cheng Hau‐Tieng Wu 72 24 0 03 Nov 2020
Graph Based Gaussian Processes on Restricted Domains David B. Dunson Hau‐Tieng Wu Nan Wu GP 58 25 0 14 Oct 2020
Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective D. Sanz-Alonso Ruiyi Yang SSL 73 10 0 26 Aug 2020
Airflow recovery from thoracic and abdominal movements using Synchrosqueezing Transform and Locally Stationary Gaussian Process Regression Whitney K. Huang Yu-Min Chung Yu-Bo Wang J. Mandel Hau‐Tieng Wu 52 5 0 11 Aug 2020
Lipschitz regularity of graph Laplacians on random data clouds Jeff Calder Nicolas García Trillos M. Lewicka 63 31 0 13 Jul 2020
Data-driven Efficient Solvers for Langevin Dynamics on Manifold in High Dimensions Yuan Gao Jiang Liu Nan Wu 48 12 0 22 May 2020
Scalability and robustness of spectral embedding: landmark diffusion is all you need Chao Shen Hau‐Tieng Wu 80 26 0 03 Jan 2020