Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise
Fuente:
arXiv
Saved in:
| Main Authors: | Cheng, Xiuyuan, Landa, Boris |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation
by: Cheng, Xiuyuan, et al.
Published: (2021)
by: Cheng, Xiuyuan, et al.
Published: (2021)
Improved convergence rate of kNN graph Laplacians: differentiable self-tuned affinity
by: Cheng, Xiuyuan, et al.
Published: (2024)
by: Cheng, Xiuyuan, et al.
Published: (2024)
Learning manifold diffusion semigroups from graph transition matrices
by: Cheng, Xiuyuan, et al.
Published: (2026)
by: Cheng, Xiuyuan, et al.
Published: (2026)
Flow-based generative models as iterative algorithms in probability space
by: Xie, Yao, et al.
Published: (2025)
by: Xie, Yao, et al.
Published: (2025)
Kernel Two-Sample Tests for Manifold Data
by: Cheng, Xiuyuan, et al.
Published: (2021)
by: Cheng, Xiuyuan, et al.
Published: (2021)
Nonsmooth Nonparametric Regression via Fractional Laplacian Eigenmaps
by: Shi, Zhaoyang, et al.
Published: (2024)
by: Shi, Zhaoyang, et al.
Published: (2024)
Asymptotic Theory of Eigenvectors for Latent Embeddings with Generalized Laplacian Matrices
by: Fan, Jianqing, et al.
Published: (2025)
by: Fan, Jianqing, et al.
Published: (2025)
Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets
by: Landa, Boris, et al.
Published: (2024)
by: Landa, Boris, et al.
Published: (2024)
Point processes with event time uncertainty
by: Cheng, Xiuyuan, et al.
Published: (2024)
by: Cheng, Xiuyuan, et al.
Published: (2024)
Deep spatio-temporal point processes: Advances and new directions
by: Cheng, Xiuyuan, et al.
Published: (2025)
by: Cheng, Xiuyuan, et al.
Published: (2025)
The G-invariant graph Laplacian
by: Rosen, Eitan, et al.
Published: (2023)
by: Rosen, Eitan, et al.
Published: (2023)
Convergence of flow-based generative models via proximal gradient descent in Wasserstein space
by: Cheng, Xiuyuan, et al.
Published: (2023)
by: Cheng, Xiuyuan, et al.
Published: (2023)
Nonlinear Laplacians: Tunable principal component analysis under directional prior information
by: Ma, Yuxin, et al.
Published: (2025)
by: Ma, Yuxin, et al.
Published: (2025)
Euclidean Distance Deflation Under High-Dimensional Heteroskedastic Noise
by: Li, Keyi, et al.
Published: (2025)
by: Li, Keyi, et al.
Published: (2025)
On damage of interpolation to adversarial robustness in regression
by: Peng, Jingfu, et al.
Published: (2026)
by: Peng, Jingfu, et al.
Published: (2026)
On the rates of convergence for learning with convolutional neural networks
by: Yang, Yunfei, et al.
Published: (2024)
by: Yang, Yunfei, et al.
Published: (2024)
Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling
by: Vukovic, Manojlo, et al.
Published: (2026)
by: Vukovic, Manojlo, et al.
Published: (2026)
Estimating the normal-inverse-Wishart distribution
by: So, Jonathan
Published: (2024)
by: So, Jonathan
Published: (2024)
Inference via robust optimal transportation: theory and methods
by: Ma, Yiming, et al.
Published: (2023)
by: Ma, Yiming, et al.
Published: (2023)
Rates of convergence for density estimation with generative adversarial networks
by: Puchkin, Nikita, et al.
Published: (2021)
by: Puchkin, Nikita, et al.
Published: (2021)
Distributed quasi-Newton robust estimation under differential privacy
by: Wang, Chuhan, et al.
Published: (2024)
by: Wang, Chuhan, et al.
Published: (2024)
Variational bagging: a robust approach for Bayesian uncertainty quantification
by: Fan, Shitao, et al.
Published: (2025)
by: Fan, Shitao, et al.
Published: (2025)
Minimax rates of convergence for nonparametric regression under adversarial attacks
by: Peng, Jingfu, et al.
Published: (2024)
by: Peng, Jingfu, et al.
Published: (2024)
An analysis of the noise schedule for score-based generative models
by: Strasman, Stanislas, et al.
Published: (2024)
by: Strasman, Stanislas, et al.
Published: (2024)
Score-based generative models are provably robust: an uncertainty quantification perspective
by: Mimikos-Stamatopoulos, Nikiforos, et al.
Published: (2024)
by: Mimikos-Stamatopoulos, Nikiforos, et al.
Published: (2024)
A theoretical framework for M-posteriors: frequentist guarantees and robustness properties
by: Marusic, Juraj, et al.
Published: (2025)
by: Marusic, Juraj, et al.
Published: (2025)
On the rate of convergence of an over-parametrized Transformer classifier learned by gradient descent
by: Kohler, Michael, et al.
Published: (2023)
by: Kohler, Michael, et al.
Published: (2023)
Online selective conformal inference: adaptive scores, convergence rate and optimality
by: Humbert, Pierre, et al.
Published: (2025)
by: Humbert, Pierre, et al.
Published: (2025)
Dynamical local Fréchet curve regression in manifolds
by: Ruiz-Medina, M. D., et al.
Published: (2025)
by: Ruiz-Medina, M. D., et al.
Published: (2025)
Low-rank matrix completion and denoising under Poisson noise
by: McRae, Andrew D., et al.
Published: (2019)
by: McRae, Andrew D., et al.
Published: (2019)
Doubly robust inference via calibration
by: van der Laan, Lars, et al.
Published: (2024)
by: van der Laan, Lars, et al.
Published: (2024)
Mixture of multilayer stochastic block models for multiview clustering
by: De Santiago, Kylliann, et al.
Published: (2024)
by: De Santiago, Kylliann, et al.
Published: (2024)
The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression
by: Hassani, Hamed, et al.
Published: (2022)
by: Hassani, Hamed, et al.
Published: (2022)
Vector-valued self-normalized concentration inequalities beyond sub-Gaussianity
by: Martinez-Taboada, Diego, et al.
Published: (2025)
by: Martinez-Taboada, Diego, et al.
Published: (2025)
Fast Spawn\&Prune (FS\&P): Global convergence of stochastic conic particle gradient descent via birth/death process
by: De Castro, Yohann, et al.
Published: (2026)
by: De Castro, Yohann, et al.
Published: (2026)
Joint stochastic localization and applications
by: Alberts, Tom, et al.
Published: (2025)
by: Alberts, Tom, et al.
Published: (2025)
Learning bounds for doubly-robust covariate shift adaptation
by: Lee, Jeonghwan, et al.
Published: (2025)
by: Lee, Jeonghwan, et al.
Published: (2025)
One-step corrected projected stochastic gradient descent for statistical estimation
by: Brouste, Alexandre, et al.
Published: (2023)
by: Brouste, Alexandre, et al.
Published: (2023)
On the sample complexity of parameter estimation in logistic regression with normal design
by: Hsu, Daniel, et al.
Published: (2023)
by: Hsu, Daniel, et al.
Published: (2023)
Sample efficient inductive matrix completion with noise and inexact side information
by: Yang, Yuepeng, et al.
Published: (2026)
by: Yang, Yuepeng, et al.
Published: (2026)
Similar Items
-
Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation
by: Cheng, Xiuyuan, et al.
Published: (2021) -
Improved convergence rate of kNN graph Laplacians: differentiable self-tuned affinity
by: Cheng, Xiuyuan, et al.
Published: (2024) -
Learning manifold diffusion semigroups from graph transition matrices
by: Cheng, Xiuyuan, et al.
Published: (2026) -
Flow-based generative models as iterative algorithms in probability space
by: Xie, Yao, et al.
Published: (2025) -
Kernel Two-Sample Tests for Manifold Data
by: Cheng, Xiuyuan, et al.
Published: (2021)