:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Denault, William R. P.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2505.11143
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

GRASP: Grouped Regression with Adaptive Shrinkage Priors
by: Tew, Shu Yu, et al.
Published: (2025)

Adaptive Shrinkage Estimation For Personalized Deep Kernel Regression In Modeling Brain Trajectories
by: Tassopoulou, Vasiliki, et al.
Published: (2025)

Shrinkage-Based Regressions with Many Related Treatments
by: Dilber, Enes, et al.
Published: (2025)

Prediction-Powered Adaptive Shrinkage Estimation
by: Li, Sida, et al.
Published: (2025)

Resampled Confidence Regions with Exponential Shrinkage for the Regression Function of Binary Classification
by: Tamás, Ambrus, et al.
Published: (2023)

Auto-Regressive U-Net for Full-Field Prediction of Shrinkage-Induced Damage in Concrete
by: Gaynutdinova, Liya, et al.
Published: (2025)

BaGGLS: A Bayesian Shrinkage Framework for Interpretable Modeling of Interactions in High-Dimensional Biological Data
by: Lemanczyk, Marta S., et al.
Published: (2025)

High-Dimensional Tail Index Regression
by: Sasaki, Yuya, et al.
Published: (2024)

On LASSO for High Dimensional Predictive Regression
by: Mei, Ziwei, et al.
Published: (2022)

Highly Adaptive Principal Component Regression
by: Wang, Mingxun, et al.
Published: (2026)

Variational Bayesian Bow tie Neural Networks with Shrinkage
by: Sheinkman, Alisa, et al.
Published: (2024)

High-Dimensional Private Linear Regression with Optimal Rates
by: Bombari, Simone, et al.
Published: (2025)

FAStEN: An Efficient Adaptive Method for Feature Selection and Estimation in High-Dimensional Functional Regressions
by: Boschi, Tobia, et al.
Published: (2023)

The Choice of Normalization Influences Shrinkage in Regularized Regression
by: Larsson, Johan, et al.
Published: (2025)

Lecture Notes on High Dimensional Linear Regression
by: Quaini, Alberto
Published: (2024)

LASSO Inference for High Dimensional Predictive Regressions
by: Gao, Zhan, et al.
Published: (2024)

Private Prediction via Shrinkage
by: Yan, Chao
Published: (2026)

Post-Transfer Learning Statistical Inference in High-Dimensional Regression
by: Tam, Nguyen Vu Khai, et al.
Published: (2025)

High-Dimensional Gaussian Process Regression with Soft Kernel Interpolation
by: Camaño, Chris, et al.
Published: (2024)

Unified Transfer Learning Models in High-Dimensional Linear Regression
by: Liu, Shuo Shuo
Published: (2023)

Neural Nonlinear Shrinkage of Covariance Matrices for Minimum Variance Portfolio Optimization
by: Yang, Liusha, et al.
Published: (2026)

Learning under Quantization for High-Dimensional Linear Regression
by: Zhang, Dechen, et al.
Published: (2025)

Scaling Laws for Precision in High-Dimensional Linear Regression
by: Zhang, Dechen, et al.
Published: (2026)

Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression
by: Kim, Juno, et al.
Published: (2025)

Admissibility of Stein Shrinkage for Batch Normalization in the Presence of Adversarial Attacks
by: Ivolgina, Sofia, et al.
Published: (2025)

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis
by: Podina, Lena, et al.
Published: (2024)

AdapDISCOM: An Adaptive Sparse Regression Method for High-Dimensional Multimodal Data With Block-Wise Missingness and Measurement Errors
by: Baldé, Maimouna, et al.
Published: (2025)

Interpretation of High-Dimensional Regression Coefficients by Comparison with Linearized Compressing Features
by: Schaeffer, Joachim, et al.
Published: (2024)

Shrinkage Initialization for Smooth Learning of Neural Networks
by: Cheng, Miao, et al.
Published: (2025)

Transfer Learning for Benign Overfitting in High-Dimensional Linear Regression
by: Kim, Yeichan, et al.
Published: (2025)

Solving Sparse \& High-Dimensional-Output Regression via Compression
by: Li, Renyuan, et al.
Published: (2024)

Transfer Learning Under High-Dimensional Network Convolutional Regression Model
by: Wang, Liyuan, et al.
Published: (2025)

Distributed High-Dimensional Quantile Regression: Estimation Efficiency and Support Recovery
by: Wang, Caixing, et al.
Published: (2024)

Stein-Rule Shrinkage for Stochastic Gradient Estimation in High Dimensions
by: Arashi, M., et al.
Published: (2026)

High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference
by: Shen, Ziliang, et al.
Published: (2025)

Adaptive Linear Embedding for Nonstationary High-Dimensional Optimization
by: Wen, Yuejiang, et al.
Published: (2025)

An Adaptive Dropout Approach for High-Dimensional Bayesian Optimization
by: Huang, Jundi, et al.
Published: (2025)

Regression Trees for Fast and Adaptive Prediction Intervals
by: Cabezas, Luben M. C., et al.
Published: (2024)

Factorized Fusion Shrinkage for Dynamic Relational Data
by: Zhao, Peng, et al.
Published: (2022)

Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization
by: Bombari, Simone, et al.
Published: (2025)