givasile / DALE

DALE: Differential Accumulated Local Effects for efficient and accurate global model explanations

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DALE: Differential Accumulated Local Effects for efficient and accurate global explanations

Accepted for publication at Asian Conference of Machine Learning (ACML) 2022. The repository contains the following four directories:

  • ./ACML-camera-ready: The camera-ready version of the paper
  • ./ACML-paper: The initial submission
  • ./ACML-rebuttal: Our answers to the issues raised by the reviewers
  • ./ACML-code: The code for reproducing all the experiments presented in the paper

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DALE: Differential Accumulated Local Effects for efficient and accurate global model explanations

License:GNU General Public License v3.0


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