Poster.pdf

que_summary.pdf
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🔗 Source: samuelbhopkins.com
📊 Size: 883 KB
📄 Pages: 1 page
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Summary

Quantum Entropy Scoring for Fast Robust Mean Estimation and Outlier Detection:
The problem involves estimating the mean of a distribution with a fraction of corrupted data.
Naïve estimates have errors that scale with dimension, and many applications require robust regression and outlier detection.
Previous methods have high time complexity, but the proposed QUE-scoring method achieves nearly linear time complexity and a dimension-independent error bound.
Experiments on synthetic, text, and image datasets demonstrate the effectiveness of QUE-scoring.
The method interpolates between scores based on l1-norm and projection onto the top eigenvalue, controlled by α, and can detect collective inductive bias.

Description

Quantum Entropy Scoring for Fast Robust Mean Estimation and Outlier Detection:
The problem involves estimating the mean of a distribution with a fraction of...

Technical Information

  • File Format: PDF
  • File Size: 883 KB
  • Pages: 1
  • Language: EN
  • Total Downloads: 179
  • Last Updated: 1 month ago

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