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.
Efficient Group Lasso Regularized Rank Regression with Simulation-Based Tuning
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.
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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.
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.
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...
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.
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.
arXiv Machine Learning
Jun 3
A Robust Optimization Approach to Sparse Principal Component Analysis
arXiv:2606. 03553v1 Announce Type: cross Abstract: While principal component analysis (PCA) is a fundamental tool for dimensionality reduction, its dense representations make it ill-suited for high-dimensional data.
arXiv Machine Learning
Jun 11
A Riemannian Approach to Low-Rank Optimal Transport
arXiv:2606. 12120v1 Announce Type: new Abstract: Low-rank optimal transport (OT) mitigates the quadratic scaling of classical solvers, yet existing approaches rely heavily on first-order mirror-descent updates that require careful hyperparameter tuning and ignore the optimization landscape's curvature.
arXiv Machine Learning
Jul 2
Optimization on the Oblique Manifold for Sparse Simplex Constraints via Multiplicative Updates
arXiv:2503. 24075v4 Announce Type: replace-cross Abstract: Low-rank optimization problems with sparse simplex constraints involve variables that must satisfy nonnegativity, sparsity, and sum-to-1 conditions, making their optimization particularly challenging due to the interplay between low-rank structures and constraints.
arXiv Machine Learning
Jul 28
Weighted Low-Rank Matrix Approximation: Acceleration and Applications
arXiv:2109. 11057v2 Announce Type: replace-cross Abstract: Weighted low-rank matrix approximation (WLRMA) generalizes classical low-rank approximation and matrix completion by allowing arbitrary elementwise weights.
arXiv Statistics ML
Aug 25
Scale-invariant Optimal Sampling for Rare-events Data with Sparse Models
arXiv:2608.22597v1 Announce Type: new Abstract: Subsampling is effective in tackling computational challenges for massive data with rare events. Overly aggressive subsampling may adversely affect est...
arXiv Machine Learning
Jul 7
Adversarial LassoNet: Robust Feature Selection via Stability-Driven Sparse Learning
arXiv:2607. 03839v1 Announce Type: new Abstract: Sparse feature selection is critical for high-dimensional machine learning, yet traditional $\ell_1$-regularized methods are often brittle under observational noise and spurious correlations, leading to unstable feature supports and degraded generalization.