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New minimax optimal estimator improves logistic regression accuracy

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv stat.ML →

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New minimax optimal estimator improves logistic regression accuracy

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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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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Junren Chen, Arya Mazumdar ·

    Minimax Optimal Estimator and Improved Error Rate for the MLE in Logistic Regression with Gaussian Design

    arXiv:2608.17260v1 Announce Type: cross Abstract: We study finite-sample parameter estimation in logistic regression with Gaussian design, where the goal is to estimate $\mathbf{\theta}^*\in \mathbb{R}^d$ with $R=\|\mathbf{\theta}^*\|_2\ge 1$ from i.i.d. samples $\{(\mathbf{x}_i,…

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