New minimax optimal estimator improves logistic regression accuracy

Researchers have developed a new minimax optimal estimator for logistic regression with Gaussian design, improving upon existing error rates. This new estimator achieves an error rate of O(sqrt(R^3/n)), which is theoretically optimal for norm estimation. The work also refines the finite-sample error rate for the maximum likelihood estimator (MLE) to \tilde{O}(sqrt(R^3/n) + R^2d/n), addressing an intrinsic bias in the MLE. Numerical experiments indicate that the proposed estimators outperform the MLE. AI

IMPACT This research advances statistical estimation techniques relevant to machine learning models.

RANK_REASON The cluster contains a single academic paper detailing a new statistical estimation method. [lever_c_demoted from research: ic=1 ai=0.7]

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• arXiv
• Candès
• Chardon
• Gaussian Design
• Lerasle
• logistic regression
• maximum likelihood estimator (MLE)
• Mourtada Gaye
• Suriname
• Zhao

• paper
• other

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