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Active Learning for Level Set Estimation Under Input Uncertainty and Its Extensions.

Yu Inatsu1, Masayuki Karasuyama2, Keiichi Inoue3

  • 1Nagoya Institute of Technology, Nagoya, Aichi, 466-8555, Japan inatsu.yu@nitech.ac.jp.

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Summary
This summary is machine-generated.

This study addresses level set estimation (LSE) under input uncertainty, crucial for manufacturing quality control. New methods are proposed to identify product property regions despite imprecise input conditions, enhancing LSE applicability.

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Area of Science:

  • Manufacturing
  • Quality Control
  • Statistical Inference

Background:

  • Product testing requires identifying conditions that ensure desired properties.
  • This is framed as level set estimation (LSE), finding input regions for a black-box function above a threshold.
  • Practical LSE faces challenges, particularly with imprecise input conditions (input uncertainty).

Purpose of the Study:

  • To introduce a framework for handling input uncertainty in LSE problems.
  • To propose efficient LSE methods with theoretical guarantees for uncertain inputs.
  • To demonstrate the applicability and effectiveness of these methods.

Main Methods:

  • Developed a general framework for LSE under input uncertainty.
  • Proposed efficient algorithms with theoretical guarantees.
  • Applied methods to artificial and real-world data.

Main Results:

  • The proposed methods effectively handle LSE problems with input uncertainty.
  • Demonstrated applicability to cost-dependent and unknown input uncertainties.
  • Validation on diverse datasets confirmed method effectiveness.

Conclusions:

  • The introduced framework and methods provide robust solutions for LSE under input uncertainty.
  • These advancements are valuable for manufacturing and quality control applications.
  • The work offers a significant step towards practical LSE in real-world scenarios.