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Gaussian Mean Estimation Faces Information-Computation Gap

By PulseAugur Editorial · [1 sources] ·

Researchers have identified an information-computation gap in estimating Gaussian means under a specific contamination model. This gap indicates that efficient algorithms require either significantly more samples than theoretically possible or incur exponential runtime. The study complements a lower bound in the Statistical Query model with an algorithm that nearly matches this tradeoff, providing a comprehensive understanding of the problem's complexity. AI

IMPACT Establishes theoretical limits for data processing under specific contamination models, potentially impacting robust AI system design.

RANK_REASON The cluster contains an academic paper detailing theoretical research findings in machine learning. [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 →

Gaussian Mean Estimation Faces Information-Computation Gap

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The cluster contains an academic paper detailing theoretical research findings in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas ·

    High-Dimensional Gaussian Mean Estimation under Realizable Contamination

    arXiv:2603.16798v2 Announce Type: replace-cross Abstract: We study mean estimation for a Gaussian distribution with identity covariance in $\mathbb{R}^d$ under a missing data scheme termed realizable $\epsilon$-contamination model. In this model an adversary can choose a function…

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