Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges

19 October 2020

Papers citing "Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges"

21 / 71 papers shown

Title
MAGIX: Model Agnostic Globally Interpretable Explanations Nikaash Puri Piyush B. Gupta Pratiksha Agarwal Sukriti Verma Balaji Krishnamurthy FAtt 91 41 0 22 Jun 2017
Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking Gabriele Tolomei Fabrizio Silvestri Andrew Haines M. Lalmas 53 208 0 20 Jun 2017
TIP: Typifying the Interpretability of Procedures Amit Dhurandhar Vijay Iyengar Ronny Luss Karthikeyan Shanmugam 30 36 0 09 Jun 2017
Interpreting Blackbox Models via Model Extraction Osbert Bastani Carolyn Kim Hamsa Bastani FAtt 64 172 0 23 May 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,864 0 22 May 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 169 2,882 0 14 Mar 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 175 5,986 0 04 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 382 3,785 0 28 Feb 2017
The Mythos of Model Interpretability Zachary Chase Lipton FaML 174 3,690 0 10 Jun 2016
Not Just a Black Box: Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Shcherbina A. Kundaje FAtt 80 788 0 05 May 2016
Distribution-Free Predictive Inference For Regression Jing Lei M. G'Sell Alessandro Rinaldo Robert Tibshirani Larry A. Wasserman 393 841 0 14 Apr 2016
Computing AIC for black-box models using Generalised Degrees of Freedom: a comparison with cross-validation Severin Hauenstein C. Dormann S. Wood AI4CE 16 18 0 09 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,954 0 16 Feb 2016
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition G. Montavon Sebastian Lapuschkin Alexander Binder Wojciech Samek Klaus-Robert Muller FAtt 60 736 0 08 Dec 2015
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model Benjamin Letham Cynthia Rudin Tyler H. McCormick D. Madigan FAtt 60 743 0 05 Nov 2015
Supersparse Linear Integer Models for Optimized Medical Scoring Systems Berk Ustun Cynthia Rudin 105 353 0 15 Feb 2015
Falling Rule Lists Fulton Wang Cynthia Rudin 52 258 0 21 Nov 2014
Deep Learning in Neural Networks: An Overview Jürgen Schmidhuber HAI 238 16,348 0 30 Apr 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 303 7,289 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 576 15,874 0 12 Nov 2013
Variable importance in binary regression trees and forests H. Ishwaran 188 385 0 15 Nov 2007