https://github.com/astrazeneca/awesome-shapley-value

Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)

https://github.com/astrazeneca/awesome-shapley-value

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
  • Academic publication links
    Links to: arxiv.org
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (7.2%) to scientific vocabulary

Keywords

artificial-intelligence data-science deep-learning explainability explainable explainable-ai explainable-artificial-intelligence explainable-ml lime machine-learning owen-value shap shapley shapley-additive-explanations shapley-decomposition shapley-q-value shapley-value xai
Last synced: 5 months ago · JSON representation

Repository

Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)

Basic Info
  • Host: GitHub
  • Owner: AstraZeneca
  • License: apache-2.0
  • Default Branch: master
  • Homepage:
  • Size: 622 KB
Statistics
  • Stars: 153
  • Watchers: 1
  • Forks: 12
  • Open Issues: 0
  • Releases: 0
Topics
artificial-intelligence data-science deep-learning explainability explainable explainable-ai explainable-artificial-intelligence explainable-ml lime machine-learning owen-value shap shapley shapley-additive-explanations shapley-decomposition shapley-q-value shapley-value xai
Created almost 4 years ago · Last pushed over 3 years ago
Metadata Files
Readme License

README.md

Awesome Shapley Value

Awesome PRs Welcome Maturity level-0

The Survey Paper

This repository accompanies our survey paper The Shapley Value in Machine Learning.

If you find the survey or this repository useful in your research, please consider citing our paper:

```bibtex @inproceedings{shapleysurvey, title = {The Shapley Value in Machine Learning}, author = {Rozemberczki, Benedek and Watson, Lauren and Bayer, Péter and Yang, Hao-Tsung and Kiss, Olivér and Nilsson, Sebastian and Sarkar, Rik}, booktitle = {Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, {IJCAI-22}}, publisher = {International Joint Conferences on Artificial Intelligence Organization}, pages = {5572--5579}, year = {2022}, }

```

Contents

  1. Game Theory Fundamentals
  2. Approximations
  3. Feature Selection
  4. Explainability
  5. Data Valuation
  6. Ensemble Selection
  7. Federated Learning
  8. Reinforcement Learning
  9. Miscellaneous

License

Owner

  • Name: AstraZeneca
  • Login: AstraZeneca
  • Kind: organization
  • Location: Global

Data and AI: Unlocking new science insights

GitHub Events

Total
  • Watch event: 11
  • Fork event: 1
Last Year
  • Watch event: 11
  • Fork event: 1