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On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks

Jul 02, 2020
Benjamin Lengerich, Eric P. Xing, Rich Caruana


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How Interpretable and Trustworthy are GAMs?

Jun 11, 2020
Chun-Hao Chang, Sarah Tan, Ben Lengerich, Anna Goldenberg, Rich Caruana


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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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Interpretable & Explorable Approximations of Black Box Models

Jul 04, 2017
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec

* Presented as a poster at the 2017 Workshop on Fairness, Accountability, and Transparency in Machine Learning 

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Do Deep Convolutional Nets Really Need to be Deep and Convolutional?

Mar 04, 2017
Gregor Urban, Krzysztof J. Geras, Samira Ebrahimi Kahou, Ozlem Aslan, Shengjie Wang, Rich Caruana, Abdelrahman Mohamed, Matthai Philipose, Matt Richardson


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Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration

Dec 10, 2016
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz

* To appear in AAAI 2017; Presented at NIPS Workshop on Reliability in ML, 2016 

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Blending LSTMs into CNNs

Sep 14, 2016
Krzysztof J. Geras, Abdel-rahman Mohamed, Rich Caruana, Gregor Urban, Shengjie Wang, Ozlem Aslan, Matthai Philipose, Matthew Richardson, Charles Sutton


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Do Deep Nets Really Need to be Deep?

Oct 11, 2014
Lei Jimmy Ba, Rich Caruana

* final revision coming soon 

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Using Multiple Samples to Learn Mixture Models

Nov 28, 2013
Jason D Lee, Ran Gilad-Bachrach, Rich Caruana

* Published in Neural Information Processing Systems (NIPS) 2013 

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