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Black-box tests for algorithmic stability.

Byol Kim1,2, Rina Foygel Barber3

  • 1Department of Biostatistics, University of Washington, 3980 15th Avenue NE, Seattle, WA 98195, USA.

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|October 16, 2023
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Summary
This summary is machine-generated.

We introduce a statistical framework for black-box testing to empirically assess algorithmic stability in machine learning. This method provides fundamental bounds on identifying stability without assumptions on data or algorithms.

Keywords:
Algorithmic stabilityassumption-free theoryblack-box algorithmshypothesis testing

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Area of Science:

  • Computer Science
  • Machine Learning
  • Statistics

Background:

  • Algorithmic stability measures how input data changes affect algorithm outputs.
  • Stability is crucial for generalization and predictive inference in machine learning.
  • Many complex algorithms resist theoretical stability analysis.

Purpose of the Study:

  • To develop a formal statistical framework for empirically assessing algorithmic stability.
  • To enable black-box testing of algorithms without prior assumptions on data or distribution.
  • To establish fundamental limits on empirical stability identification.

Main Methods:

  • Developed a formal statistical framework for black-box testing.
  • Focused on empirical evaluation of algorithmic behavior on diverse datasets.
  • Established theoretical bounds on the efficacy of black-box stability testing.

Main Results:

  • A novel statistical framework for black-box algorithmic stability testing is presented.
  • Fundamental bounds were established for empirical stability identification.
  • The approach allows stability assessment of complex algorithms lacking theoretical analysis.

Conclusions:

  • Empirical black-box testing provides a viable method for assessing algorithmic stability.
  • The established bounds inform the limitations and capabilities of such testing.
  • This framework facilitates understanding the stability of complex, modern algorithms.