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Researchers propose new method to stabilize private LASSO under heterogeneous covariates

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method to stabilize the LASSO algorithm when dealing with heterogeneous covariate scales under differential privacy constraints. Their approach, termed Gram-based anisotropic objective perturbation, aims to counteract distortions caused by varying covariate structures without consuming additional privacy budget. This technique, analyzed using an Approximate Message Passing framework, reportedly enhances convergence stability and improves both statistical efficiency and privacy performance compared to traditional uniform noise injection. AI

IMPACT Introduces a novel technique for enhancing privacy in statistical estimation without sacrificing efficiency.

RANK_REASON Academic paper on a novel statistical method for privacy-preserving machine learning.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Researchers propose new method to stabilize private LASSO under heterogeneous covariates

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Haruka Tanzawa, Ayaka Sakata ·

    Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation

    arXiv:2605.01492v1 Announce Type: new Abstract: 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 problemat…

  2. arXiv stat.ML TIER_1 English(EN) · Ayaka Sakata ·

    Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation

    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 add…

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