Cover Image

Generalisation and benign over-fitting for linear regression onto random functional covariates

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jones, Andrew, Whiteley, Nick
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Hidden Markov Neural Networks
by: Rimella, Lorenzo, et al.
Published: (2020)

How high is `high'? Rethinking the roles of dimensionality in topological data analysis and manifold learning
by: Sansford, Hannah, et al.
Published: (2025)

Uncovering Locally Low-dimensional Structure in Networks by Locally Optimal Spectral Embedding
by: Sansford, Hannah, et al.
Published: (2026)

Estimation of sparse linear regression coefficients under $L$-subexponential covariates
by: Sasai, Takeyuki
Published: (2023)

Bilateral Distribution Compression: Reducing Both Data Size and Dimensionality
by: Broadbent, Dominic, et al.
Published: (2025)

Accounting for shared covariates in semi-parametric Bayesian additive regression trees
by: Prado, Estevão B., et al.
Published: (2021)

Hierarchical clustering with dot products recovers hidden tree structure
by: Gray, Annie, et al.
Published: (2023)

Conditional Distribution Compression via the Kernel Conditional Mean Embedding
by: Broadbent, Dominic, et al.
Published: (2025)

Convex estimation of Gaussian graphical regression models with covariates
by: Liu, Ruobin, et al.
Published: (2024)

The Origins of Representation Manifolds in Large Language Models
by: Modell, Alexander, et al.
Published: (2025)

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

A finite sample analysis of the benign overfitting phenomenon for ridge function estimation
by: Caron, Emmanuel, et al.
Published: (2020)

Transfer learning for functional linear regression via control variates
by: Yang, Yuping, et al.
Published: (2026)

Meta-learning of shared linear representations beyond well-specified linear regression
by: Even, Mathieu, et al.
Published: (2025)

BARTSIMP: flexible spatial covariate modeling and prediction using Bayesian additive regression trees
by: Jiang, Alex Ziyu, et al.
Published: (2023)

Augmented balancing weights as linear regression
by: Bruns-Smith, David, et al.
Published: (2023)

Semi-supervised learning for linear extremile regression
by: Jiang, Rong, et al.
Published: (2025)

A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression
by: Kim, Youngseok, et al.
Published: (2022)

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

A class of modular and flexible covariate-based covariance functions for nonstationary spatial modeling
by: Blasi, Federico, et al.
Published: (2024)

Do Generalisation Results Generalise?
by: Boglioni, Matteo, et al.
Published: (2025)

Deep linear networks for regression are implicitly regularized towards flat minima
by: Marion, Pierre, et al.
Published: (2024)

Nonlinear functional regression by functional deep neural network with kernel embedding
by: Shi, Zhongjie, et al.
Published: (2024)

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

Nesterov acceleration in benignly non-convex landscapes
by: Gupta, Kanan, et al.
Published: (2024)

Fast approximative estimation of conditional Shapley values when using a linear regression model or a polynomial regression model
by: Aanes, Fredrik Lohne
Published: (2025)

Discovery and inference beyond linearity by integrating Bayesian regression, tree ensembles and Shapley values
by: Spadaccini, Giorgio, et al.
Published: (2025)

Analytical results for uncertainty propagation through trained machine learning regression models
by: Thompson, Andrew
Published: (2024)

A randomized algorithm to solve reduced rank operator regression
by: Turri, Giacomo, et al.
Published: (2023)

A semi-supervised learning using over-parameterized regression
by: Hagiwara, Katsuyuki
Published: (2024)

Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks
by: Modell, Alexander, et al.
Published: (2023)

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

Symmetry and Generalisation in Machine Learning
by: Elesedy, Hayder
Published: (2025)

Asymptotic non-linear shrinkage and eigenvector overlap for weighted sample covariance
by: Oriol, Benoit
Published: (2024)

Clustered random forests with correlated data for optimal estimation and inference under potential covariate shift
by: Young, Elliot H., et al.
Published: (2025)

Fréchet random forests for metric space valued regression with non euclidean predictors
by: Capitaine, Louis, et al.
Published: (2019)

Statistical exploration of the Manifold Hypothesis
by: Whiteley, Nick, et al.
Published: (2022)

Data driven feedback linearization of nonlinear control systems via Lie derivatives and stacked regression approach
by: K., Lakshmi Priya P., et al.
Published: (2025)

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

Outlier-robust Kalman Filtering through Generalised Bayes
by: Duran-Martin, Gerardo, et al.
Published: (2024)