:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Yuan, Changhui, Zhao, Shishun, Li, Shuwei, Song, Xinyuan, Chen, Zhao
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Statistics Theory
Online Access:	https://arxiv.org/abs/2503.19763
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Interpretable Deep Regression Models With Interval‐Censored Failure Time Data
by: Changhui Yuan, et al.
Published: (2026)

Sieve Maximum Likelihood Estimation of Partially Linear Transformation Models With Interval‐Censored Data
by: Changhui Yuan, et al.
Published: (2024)

Mediation Analysis of Failure Time Data Under Interval Censoring
by: Yanpeng Shi, et al.
Published: (2026)

Learning Robust Treatment Rules for Censored Data
by: Cui, Yifan, et al.
Published: (2024)

Learning Survival Models with Right-Censored Reporting Delays
by: Shikuri, Yuta, et al.
Published: (2025)

Learning High-dimensional Gaussians from Censored Data
by: Bhattacharyya, Arnab, et al.
Published: (2025)

Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics
by: de Souza, Douglas Baptista, et al.
Published: (2024)

Doubly Robust Interval Estimation for Optimal Policy Evaluation in Online Learning
by: Shen, Ye, et al.
Published: (2021)

An Augmented Approach for Regression Analysis of Case‐Cohort Interval‐Censored Failure Time Data
by: Yichen Lou, et al.
Published: (2025)

Interval Fisher's Discriminant Analysis and Visualisation
by: Pinheiro, Diogo, et al.
Published: (2025)

Adaptive Confidence Intervals in Efron's Gaussian Two-Groups Model
by: Wang, Qiaosen, et al.
Published: (2026)

Nonparametric Instrumental Variable Regression with Observed Covariates
by: Shen, Zikai, et al.
Published: (2025)

Improved Scaling Laws in Linear Regression via Data Reuse
by: Lin, Licong, et al.
Published: (2025)

Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation
by: Morisset, Lucas, et al.
Published: (2026)

Online Quantile Regression for Nonparametric Additive Models
by: Zhan, Haoran
Published: (2026)

Smaller Confidence Intervals From IPW Estimators via Data-Dependent Coarsening
by: Kalavasis, Alkis, et al.
Published: (2024)

Generalization for Least Squares Regression With Simple Spiked Covariances
by: Li, Jiping, et al.
Published: (2024)

UTOPIA: Universally Trainable Optimal Prediction Intervals Aggregation
by: Fan, Jianqing, et al.
Published: (2023)

Self-Normalized Martingales and Uniform Regret Bounds for Linear Regression
by: Chen, Fan, et al.
Published: (2026)

Asymptotic Optimism for Tensor Regression Models with Applications to Neural Network Compression
by: Shi, Haoming, et al.
Published: (2026)

Least Squares Regression Can Exhibit Under-Parameterized Double Descent
by: Li, Xinyue, et al.
Published: (2023)

Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation
by: Mallory, Matthew Esmaili, et al.
Published: (2025)

Optimal Aggregation of Prediction Intervals under Unsupervised Domain Shift
by: Ge, Jiawei, et al.
Published: (2024)

Semi-supervised Fréchet Regression
by: Qiu, Rui, et al.
Published: (2024)

Kernel Ridge Regression Inference
by: Singh, Rahul, et al.
Published: (2023)

Adaptation to Intrinsic Dependence in Diffusion Language Models
by: Zhao, Yunxiao, et al.
Published: (2026)

Inference for Deep Neural Network Estimators in Generalized Nonparametric Models
by: Meng, Xuran, et al.
Published: (2025)

Scaling Laws in Linear Regression: Compute, Parameters, and Data
by: Lin, Licong, et al.
Published: (2024)

The Catastrophic Failure of The k-Means Algorithm in High Dimensions, and How Hartigan's Algorithm Avoids It
by: Lederman, Roy R., et al.
Published: (2026)

A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models
by: Kumar, Shivam, et al.
Published: (2024)

Proportional Mean Residual Life Model With Varying Coefficients for Right Censored Data
by: Bing Wang, et al.
Published: (2025)

Sparse Max-Affine Regression
by: Kanj, Haitham, et al.
Published: (2024)

Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent
by: Patil, Pratik, et al.
Published: (2024)

On Nonasymptotic Confidence Intervals for Treatment Effects in Randomized Experiments
by: Sandoval, Ricardo J., et al.
Published: (2026)

Agnostic Sample Compression Schemes for Regression
by: Attias, Idan, et al.
Published: (2018)

Max-Linear Regression by Convex Programming
by: Kim, Seonho, et al.
Published: (2021)

Navigating Sparsities in High-Dimensional Linear Contextual Bandits
by: Zhao, Rui, et al.
Published: (2025)

Active Learning via Regression Beyond Realizability
by: Ganju, Atul, et al.
Published: (2025)

Debiased Bayesian Inference for High-dimensional Regression Models
by: Chen, Qihui, et al.
Published: (2025)

Minimax Linear Regression under the Quantile Risk
by: Hanchi, Ayoub El, et al.
Published: (2024)