Bibliography
How to cite Khiops
If you use Khiops in your research, papers, or projects, please cite our main reference article:
Khiops: An End-to-End Frugal AutoML and XAI Machine Learning Solution for Large Multi-Table Databases - download
BibTeX Citation
@inproceedings{boulle2025khiopscaid,
author = {Marc Boull{\'e} and Nicolas Voisine and Bruno Guerraz and Carine Hue and Felipe Olmos and Vladimir Popescu and St{\'e}phane Gouache and St{\'e}phane Bouget and Alexis Bondu and Luc Aur{\'e}lien Gauthier and Yassine Nair Benrekia and Fabrice Cl{\'e}rot and Vincent Lemaire},
title = {Khiops: An End-to-End, Frugal {AutoML} and {XAI} Machine Learning Solution for Large, Multi-Table Databases},
booktitle = {Proceedings of the Conference on Artificial Intelligence for Defense (CAID)},
year = {2025},
month = {November},
address = {Rennes, France},
url = {https://caid-conference.eu}
}
To go further, here is a selection of scientific papers organized as a reading path designed to clarify the AutoML pipeline. We highly recommend reading them in the suggested order, after exploring the documentation provided on this website.
More than a hundred articles about Khiops are available on this page.
Suggested Reading Path
The gray entries indicate complementary material that can be read later on, without hindering your overall understanding of the pipeline.
Optimal Encoding
- Discretization models: MODL: a Bayes optimal discretization method for continuous attributes - download
- Grouping models: A Bayes optimal approach for partitioning the values of categorical attributes - download
- The regression case: A New Probabilistic Approach In Rank Regression with Optimal Bayesian Partitioning - download
Auto Feature Engineering
- Multi-table data: A scalable robust and automatic propositionalization approach for Bayesian classification of large mixed numerical and categorical data - download
- Decision trees: A Bayes Evaluation Criterion for Decision Trees - download
- Pair discretization: Optimum simultaneous discretization with data grid models in supervised classification: a Bayesian model selection approach - download