InterpretML: A Unified Framework for Machine Learning Interpretability

19 September 2019

Papers citing "InterpretML: A Unified Framework for Machine Learning Interpretability"

8 / 8 papers shown

Title
Predicting Census Survey Response Rates With Parsimonious Additive Models and Structured Interactions Shibal Ibrahim P. Radchenko E. Ben-David Rahul Mazumder 214 2 0 24 Aug 2021
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models Ian Tenney James Wexler Jasmijn Bastings Tolga Bolukbasi Andy Coenen ... Ellen Jiang Mahima Pushkarna Carey Radebaugh Emily Reif Ann Yuan VLM 101 192 0 12 Aug 2020
An Interpretable Model with Globally Consistent Explanations for Credit Risk Chaofan Chen Kangcheng Lin Cynthia Rudin Yaron Shaposhnik Sijia Wang Tong Wang FAtt 40 93 0 30 Nov 2018
Axiomatic Interpretability for Multiclass Additive Models Xuezhou Zhang S. Tan Paul Koch Yin Lou Urszula Chajewska R. Caruana FAtt AI4CE 23 3 0 22 Oct 2018
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation S. Tan R. Caruana Giles Hooker Yin Lou MLAU 101 184 0 17 Oct 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 546 21,613 0 22 May 2017
XGBoost: A Scalable Tree Boosting System Tianqi Chen Carlos Guestrin 361 37,815 0 09 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 587 16,828 0 16 Feb 2016