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Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes


Jul 12, 2022
Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Kristin Sitcov, Vivienne Souter, Rich Caruana

* DSHealth at SIGKDD 2022, 5 pages, 3 figures 

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Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values


Jun 30, 2022
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana

* Accepted at KDD 2022. 11 pages, 19 figures. For a demo video, see https://youtu.be/D6whtfInqTc. For a live demo, visit https://interpret.ml/gam-changer 

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Differentially Private Estimation of Heterogeneous Causal Effects


Feb 22, 2022
Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, Aadharsh Kannan


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GAM Changer: Editing Generalized Additive Models with Interactive Visualization


Dec 06, 2021
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana

* 7 pages, 15 figures, accepted to the Research2Clinics workshop at NeurIPS 2021. For a demo video, see https://youtu.be/2gVSoPoSeJ8. For a live demo, visit https://interpret.ml/gam-changer/ 

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Accuracy, Interpretability, and Differential Privacy via Explainable Boosting


Jun 17, 2021
Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni

* To be published in ICML 2021. 12 pages, 6 figures 

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InterpretML: A Unified Framework for Machine Learning Interpretability


Sep 19, 2019
Harsha Nori, Samuel Jenkins, Paul Koch, Rich Caruana


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