AI RESEARCH
Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation
arXiv CS.LG
•
ArXi:2605.01492v1 Announce Type: cross We study high-dimensional LASSO under differential privacy via objective perturbation with heterogeneous covariate scales. In practical scenarios, covariates often exhibit diverse scales; however, standard preprocessing is problematic under privacy constraints, as it consumes additional privacy budget. This heterogeneity induces effective anisotropy in the objective perturbation via the inverse Gram matrix of covariates, which can degrade the stability and accuracy of algorithms.