arXiv Machine Learning July 14, 2026 By Meixia Lin, Mengjiao Shi, Yunhai Xiao, Qian Zhang

Efficient Group Lasso Regularized Rank Regression with Simulation-Based Tuning

Read the original on arXiv Machine Learning →

arXiv:2510. 11546v3 Announce Type: replace-cross Abstract: High-dimensional regression often suffers from heavy-tailed noise and outliers, which can severely undermine the reliability of least-squares based methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Aug 24

Primal Acceleration of Newton's Method

arXiv:2608. 21359v1 Announce Type: cross Abstract: We develop a new direct accelerated Newton method for minimizing convex functions with Lipschitz continuous Hessian.

By Nikita Doikov

arXiv Machine Learning

Jul 7

Efficient Cross-Validation for Sparse Linear Regression

arXiv:2306. 14851v5 Announce Type: replace-cross Abstract: Given a high-dimensional covariate matrix and a response vector, ridge-regularized sparse linear regression selects a subset of features that explains the relationship between covariates and the response in an interpretable manner.

By Ryan Cory-Wright, Andr\'es G\'omez

efficiency

arXiv Machine Learning

Jun 11

Renewable Lasso without Batch-Number Constraints: A Gradient-Enhanced Approach

arXiv:2606. 11738v1 Announce Type: cross Abstract: We study online estimation for high-dimensional generalized linear models with streaming data.

By Junzhuo Gao, Ling Peng, Xu Guo, Heng Lian

arXiv AI

Sep 10

Sparse Data Augmentation for Optimization with Provable Guarantees

arXiv:2609.08133v1 Announce Type: cross Abstract: In nonconvex optimization problems arising in geometric machine learning, data augmentation is commonly used to promote invariance by averaging empir...

By Behrooz Tahmasebi, Melanie Weber

arXiv Machine Learning

Jul 13

Near-optimal Delta-convex Estimation of Lipschitz Functions

arXiv:2511. 15615v2 Announce Type: replace-cross Abstract: This paper presents a tractable algorithm for estimating an unknown Lipschitz function from noisy observations and establishes an upper bound on its convergence rate.

By G\'abor Bal\'azs

arXiv Machine Learning

Jul 7

Local Constrained Bayesian Optimization

arXiv:2603. 07965v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) for high-dimensional constrained problems remains a significant challenge due to the curse of dimensionality.

By Jing Jingzhe, Fan Zheyi, Szu Hui Ng, Qingpei Hu

benchmarks

One email a morning, machine-written

Related stories

Primal Acceleration of Newton's Method

Efficient Cross-Validation for Sparse Linear Regression

Renewable Lasso without Batch-Number Constraints: A Gradient-Enhanced Approach

Sparse Data Augmentation for Optimization with Provable Guarantees

Near-optimal Delta-convex Estimation of Lipschitz Functions

Local Constrained Bayesian Optimization