arXiv research

Unified derivation of high-dimensional linear models using stochastic gradient descent.

Review of privacy-preserving linear models for high-dimensional data.

Nested model averaging improves high-dimensional linear regression performance.

Kernel methods and MLPs perform similarly to linear models in high dimensions.

Develops a method for learning sparse generalized linear models in high-dimensional data.

Paper introduces semi-supervised linear extremile regression for high-dimensional data.

Study high-dimensional Bayesian linear regression using variational inference.

Develops generic spike-and-slab priors for high-dimensional linear regression.

FLIPHAT addresses joint differential privacy for high-dimensional sparse linear bandits.

Develops a gradient-enhanced approach for online estimation in high-dimensional generalized linear models with streaming data.

Develops inequalities for high-dimensional linear processes with dependent innovations.

The paper develops methods to create reliable prediction sets for complex mixture models in high-dimensional data.

PROBE algorithm efficiently solves sparse high-dimensional linear regression.

Estimates CATEs using high-dimensional linear regression models.

Efficient Bayesian LMM framework for high-dimensional longitudinal data.

The paper analyzes sparse high-dimensional linear regression with random design and unknown error variance, providing adaptiveness and concentration rates.

This paper extends RMT for deep learning models beyond eigenvalues.

New AMP algorithm detects change points in high-dimensional GLMs.

This paper examines linear embeddings for high-dimensional Bayesian optimization, identifying and addressing issues to improve performance.

Modeling dynamical systems is important in many disciplines, e.g., control, robotics, or neurotechnology. Commonly the state of these systems is not directly observed, but only available through noisy and potentially high-dimensional observations. In these cases, system identification, i.e., finding the measurement map…

Recent methods for estimating sparse undirected graphs for real-valued data in high dimensional problems rely heavily on the assumption of normality. We show how to use a semiparametric Gaussian copula--or "nonparanormal"--for high dimensional inference. Just as additive models extend linear models by replacing linear …

This work studies scaling laws for low-precision training in high-dimensional linear regression.

A neural network model tackles high-dimensional data with latent structures.

New bounds for high-dimensional sparse linear bandits, balancing information and regret.

SCOPE fuses categorical variable levels to estimate high-dimensional linear models.

Simple linear models outperform complex BO methods in high dimensions.

A scalable method for accurate inference of low-dimensional parameters in high-dimensional linear regression.

Improved ridge estimators avoid tuning parameters for high-dimensional data.

We simplify complex regression coefficients using linearization and feature comparison.

We solve a high-dimensional model where nonlinear autoencoders detect hidden structure missed by PCA.

Improved Thompson Sampling for high-dimensional sparse bandits.

The paper tackles high-dimensional mixed linear regression with unknown parameters and proposes methods for estimation, confidence intervals, and hypothesis testing.

Forward regression is a statistical model selection and estimation procedure which inductively selects covariates that add predictive power into a working statistical regression model. Once a model is selected, unknown regression parameters are estimated by least squares. This paper analyzes forward regression in high-…

Sequential coordinate ascent is more robust in high-dimensional linear regression.

SVM and linear regression models coincide in high dimensions.

The nullspace and regularization impact high-dimensional linear regression interpretability.

A scalable model for high-dimensional longitudinal data.

The study examines the universality of Gaussian data in high-dimensional generalized linear estimation.

New model separates images into independent factors quickly and easily.

Develops a data-driven smoothing technique for high-dimensional, non-linear panel data.

Modern data analysis depends increasingly on estimating models via flexible high-dimensional or nonparametric machine learning methods, where the identification of structural parameters is often challenging and untestable. In linear settings, this identification hinges on the completeness condition, which requires the …

Study of Bayes optimal learning in high-dimensional linear regression with network side information.

Study dynamic batch learning in high-dimensional sparse linear bandits.

We prove that L2-Boosting lacks a theoretical property which is central to the behaviour of l1-penalized methods such as basis pursuit and the Lasso: Whereas l1-penalized methods are guaranteed to recover the sparse parameter vector in a high-dimensional linear model under an appropriate restricted nullspace property, …

Study shows double descent curve in high-dimensional linear regression with random projections.

Linear classification has been widely used in many high-dimensional applications like text classification. To perform linear classification for large-scale tasks, we often need to design distributed learning methods on a cluster of multiple machines. In this paper, we propose a new distributed learning method, called f…

New research shows CFG improves high-dimensional data generation.

Paper develops a method for estimating PFLM with minimized rates in high dimensions.