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Neural Additive Models: Interpretable Machine Learning with Neural Nets


Apr 29, 2020
Rishabh Agarwal, Nicholas Frosst, Xuezhou Zhang, Rich Caruana, Geoffrey E. Hinton


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Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere


Mar 15, 2020
Jonathan A. Weyn, Dale R. Durran, Rich Caruana

* Manuscript submitted to Journal of Advances in Modeling Earth Systems 

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Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models


Nov 12, 2019
Benjamin Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana


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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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Efficient Forward Architecture Search


May 31, 2019
Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric Horvitz, Debadeepta Dey

* preprint 

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Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models


Oct 22, 2018
Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana

* Preprint 

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Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation


Oct 11, 2018
Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou

* Camera-ready version for AAAI/ACM AIES 2018. Data and pseudocode at https://github.com/shftan/auditblackbox. Previously titled "Detecting Bias in Black-Box Models Using Transparent Model Distillation". A short version was presented at NIPS 2017 Symposium on Interpretable Machine Learning 

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Sparse Partially Linear Additive Models


Mar 27, 2018
Yin Lou, Jacob Bien, Rich Caruana, Johannes Gehrke

* Corrected typos 

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Transparent Model Distillation


Jan 26, 2018
Sarah Tan, Rich Caruana, Giles Hooker, Albert Gordo


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Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning


Dec 12, 2017
Andrew Gordon Wilson, Jason Yosinski, Patrice Simard, Rich Caruana, William Herlands

* 25 papers 

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