Active Model Adaptation Under Unknown Shift..pdf

kdd22-auto.pdf
Preview of Active Model Adaptation Under Unknown Shift.
🔗 Source: cs.nju.edu.cn
📊 Size: 1.23 MB
📄 Pages: 9 pages
⬇️ Downloads: 185

Summary

Machine learning models typically rely on a fixed distribution, but data distribution shift is commonly observed in real-world practice due to unforeseen environmental shifts. To cope with this problem, known as Resource Constrained Adaptation under Unknown Shift, the authors study active model adaptation both theoretically and empirically. They present a generalization analysis of active model adaptation for distribution shift, showing that it can improve the generalization error from O(1/√N) to O(1/N) with only a few queried samples. The authors also propose a systemic solution, Auto, which consists of distribution tracking, sample selection, and model adaptation, and demonstrate its effectiveness through empirical studies on diverse real-world data sets, outperforming nine existing active strategies.

Description

Machine learning models typically rely on a fixed distribution, but data distribution shift is commonly observed in real-world practice due to unforeseen...

Technical Information

  • File Format: PDF
  • File Size: 1.23 MB
  • Pages: 9
  • Language: EN
  • Total Downloads: 185
  • Last Updated: 4 weeks ago

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