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Sarah Tan

Distilling Heterogeneity: From Explanations of Heterogeneous Treatment Effect Models to Interpretable Policies

Nov 05, 2021
Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel Jiang, Eytan Bakshy

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* A short version was presented at MIT CODE 2021

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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

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* 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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A Double Parametric Bootstrap Test for Topic Models

Nov 21, 2017
Skyler Seto, Sarah Tan, Giles Hooker, Martin T. Wells

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* Presented at NIPS 2017 Symposium on Interpretable Machine Learning

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