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  • Avishek Anand
  • interpretability
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Last edited by Avishek Anand Feb 27, 2020
Page history

Home

Contents

  • Limitations of Interpretability
  • NeurIPS 2019 Interpretability Roundup
  • Interpretability by Design
  • Reinforcement Learning for NLP and Text
  • Tutorials and Introductory remarks
  • Concept-based Explanations

Resources

Tutorials

Title Venue Authors slides
Interpretable ML: The fuss, the concrete and the questions ICML 2017 Kim and Doshi-Velez link
Tutorial on XKDD XKDD Workshop ECML 2018 Monreale et al. link
Explainable AI in Industry KDD 2019 Geyik et al., LinkedIn link
On Explainable AI:From Theory to Motivation, Applications and Limitations AAAI 2019 many link

Overviews, Surveys, Reviews

Title Author link
The Mythos of Model Interpretability Lipton arxiv
Towards a Rigorous Science of Interpretable Machine Learning Doshi-Velez and Kim arxiv
A Survey of Methods for Explaining Black Box Models Guidotti et al. arxiv
Interpretability of Machine Learning Models and Representations: an Introduction Adrien Bibal and Benoît Frénay pdf

Books

Title Author link
Interpretable ML: A Guide for Making Black Box Models Explainable. Molnar link
Limitations of Interpretable Machine Learning Methods LMU seminar link

Editing & browsing this wiki locally using vscode:

  • clone wiki:
    git clone git@git.l3s.uni-hannover.de:anand/interpretability.wiki.git
    cd interpretability.wiki
  • install vscode extension yzhang.markdown-all-in-one
  • install vscode extension goessner.mdmath
  • go to user settings, search for mdmath and set Delimiters to 'gitlab'
  • The vscode markdown preview should now render according to the gitlab markdown standard
  • commit and push changes using git
  • images can be put into the uploads folder and refered to as ./uploads/image.png
Clone repository
  • Concept based Explanations
  • Interpretability By Design
  • Limitations of Interpretability
  • Neurips 2019 Interpretability Roundup
  • On the (In)fidelity and Sensitivity of Explanations
  • Re inforcement Learning for NLP and Text
  • Tutorials and Introductory remarks
  • Visualizing and Measuring the Geometry of BERT
  • a benchmark for interpretability methods in deep neural networks
  • bam
  • explanations can be manipulated and geometry is to blame
  • Home