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New research links MLE and control variates in machine learning algorithms

By PulseAugur Editorial · [1 sources] ·

Researchers have established a theoretical equivalence between Maximum Likelihood Estimation (MLE) and Control Variate Estimators (CVE) within sketching algorithms for machine learning. This equivalence, demonstrated under specific conditions in an exponential family, suggests that an optimal CVE can achieve the same asymptotic variance as an MLE, thereby enabling a fixed-point algorithm for MLE. Experimental results indicate this fixed-point approach offers improved speed and numerical stability compared to traditional root-finding methods for MLE, particularly for bivariate normal distributions. AI

IMPACT This research could lead to more efficient and reproducible methods for training machine learning models that rely on maximum likelihood estimation.

RANK_REASON The cluster contains a single academic paper detailing a theoretical finding in machine learning statistics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research links MLE and control variates in machine learning algorithms

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The cluster contains a single academic paper detailing a theoretical finding in machine learning statistics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Keegan Kang, Kerong Wang, Ding Zhang, Rameshwar Pratap, Bhisham Dev Verma, Benedict H. W. Wong ·

    It's all In the (Exponential) Family: An Equivalence between Maximum Likelihood Estimation and Control Variates for Sketching Algorithms

    arXiv:2601.22378v3 Announce Type: replace Abstract: Maximum likelihood estimators (MLE) and control variate estimators (CVE) have been used in conjunction with known information across sketching algorithms and applications in machine learning. We prove that under certain conditio…

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