Cover Image

Testing for correlation between network structure and high-dimensional node covariates

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Fuchs-Kreiss, Alexander, Levin, Keith
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Adjacency Spectral Embeddings of Correlation Networks
by: Levin, Keith
Published: (2026)

Correlation bounds, mixing and m-dependence under random time-varying network distances with an application to Cox-Processes
by: Kreiss, Alexander
Published: (2019)

Testing properties of trees in graphical models with covariance queries
by: Burova, Sofiya, et al.
Published: (2026)

Bayes optimal learning in high-dimensional linear regression with network side information
by: Nandy, Sagnik, et al.
Published: (2023)

Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation
by: Jin, Yanhao, et al.
Published: (2024)

Nonlinear spiked covariance matrices and signal propagation in deep neural networks
by: Wang, Zhichao, et al.
Published: (2024)

Powerful rank verification for multivariate Gaussian data with any covariance structure
by: Sood, Anav
Published: (2025)

Consistent support recovery for high-dimensional diffusions
by: Marushkevych, Dmytro, et al.
Published: (2025)

Matching and mixing: Matchability of graphs under Markovian error
by: Li, Zhirui, et al.
Published: (2026)

Support estimation in high-dimensional heteroscedastic mean regression
by: Hermann, Philipp, et al.
Published: (2020)

Minimax optimal transfer learning for high-dimensional additive regression
by: Moon, Seung Hyun
Published: (2025)

Sliced gradient-enhanced Kriging for high-dimensional function approximation
by: Cheng, Kai, et al.
Published: (2022)

Sliding down the stairs: how correlated latent variables accelerate learning with neural networks
by: Bardone, Lorenzo, et al.
Published: (2024)

Parametric Mean-Field empirical Bayes in high-dimensional linear regression
by: Lee, Seunghyun, et al.
Published: (2026)

A sparse PAC-Bayesian approach for high-dimensional quantile prediction
by: Mai, The Tien
Published: (2024)

Precise Error Rates for Computationally Efficient Testing
by: Moitra, Ankur, et al.
Published: (2023)

Scalable multitask Gaussian processes for complex mechanical systems with functional covariates
by: Gninkou, Razak Christophe Sabi, et al.
Published: (2026)

Gaussian Mixture Model with unknown diagonal covariances via continuous sparse regularization
by: Giard, Romane, et al.
Published: (2025)

Closing the problem of which causal structures of up to six total nodes have a classical-quantum gap
by: Khanna, Shashaank, et al.
Published: (2025)

Testing For Global Covariate Effects in Dynamic Interaction Event Networks
by: Kreiss, Alexander, et al.
Published: (2021)

High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance
by: Balasubramanian, Krishnakumar, et al.
Published: (2023)

Concentration of a sparse Bayesian model with Horseshoe prior in estimating high-dimensional precision matrix
by: Mai, The Tien
Published: (2024)

Learning bounds for doubly-robust covariate shift adaptation
by: Lee, Jeonghwan, et al.
Published: (2025)

Augmented transfer regression learning for completely missing covariates
by: Zhao, Huali, et al.
Published: (2026)

A variational Bayes approach to debiased inference for low-dimensional parameters in high-dimensional linear regression
by: Castillo, Ismaël, et al.
Published: (2024)

Universality of high-dimensional scaling limits of stochastic gradient descent
by: Gheissari, Reza, et al.
Published: (2025)

Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set
by: Choo, Davin, et al.
Published: (2024)

Analysis of a multi-target linear shrinkage covariance estimator
by: Oriol, Benoit
Published: (2024)

Adaptive posterior concentration rates for sparse high-dimensional linear regression with random design and unknown error variance
by: Mai, The Tien
Published: (2024)

Scalability of Metropolis-within-Gibbs schemes for high-dimensional Bayesian models
by: Ascolani, Filippo, et al.
Published: (2024)

On the distance between mean and geometric median in high dimensions
by: Schwank, Richard, et al.
Published: (2025)

Optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization
by: Kobak, Dmitry, et al.
Published: (2018)

Asymptotic spectrum of weighted sample covariance: another proof of spectrum convergence
by: Oriol, Benoit
Published: (2024)

Conjugate gradient methods for high-dimensional GLMMs
by: Pandolfi, Andrea, et al.
Published: (2024)

Entropic covariance models
by: Zwiernik, Piotr
Published: (2023)

On Transferring Transferability: Towards a Theory for Size Generalization
by: Levin, Eitan, et al.
Published: (2025)

Fundamental limits of distributed covariance matrix estimation via a conditional strong data processing inequality
by: Rahmani, Mohammad Reza, et al.
Published: (2025)

Matching correlated VAR time series
by: Araya, Ernesto, et al.
Published: (2025)

Dimension-free bounds in high-dimensional linear regression via error-in-operator approach
by: Noskov, Fedor, et al.
Published: (2025)

Local geometry of high-dimensional mixture models: Effective spectral theory and dynamical transitions
by: Arous, Gerard Ben, et al.
Published: (2025)