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Distilling Interpretable Models into Human-Readable Code

Feb 09, 2021
Walker Ravina, Ethan Sterling, Olexiy Oryeshko, Nathan Bell, Honglei Zhuang, Xuanhui Wang, Yonghui Wu, Alexander Grushetsky

* 13 pages, Latex; Updated the introduction and preliminaries sections; Updated some figures for greater clarity and brevity; Added a new dataset to the experiments; Added a more detailed table of experiment results; Added a discussion of distillation failures to the experiments relating to the new dataset 

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Interpretable Learning-to-Rank with Generalized Additive Models

May 14, 2020
Honglei Zhuang, Xuanhui Wang, Michael Bendersky, Alexander Grushetsky, Yonghui Wu, Petr Mitrichev, Ethan Sterling, Nathan Bell, Walker Ravina, Hai Qian

* 10 pages 

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TF Boosted Trees: A scalable TensorFlow based framework for gradient boosting

Oct 31, 2017
Natalia Ponomareva, Soroush Radpour, Gilbert Hendry, Salem Haykal, Thomas Colthurst, Petr Mitrichev, Alexander Grushetsky

* European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2017). The final publication will be available at and is available on ECML website 

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