Information-theoretic Evolution of Model Agnostic Global Explanations

14 May 2021

Papers citing "Information-theoretic Evolution of Model Agnostic Global Explanations"

25 / 25 papers shown

Title
"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations Himabindu Lakkaraju Osbert Bastani 68 255 0 15 Nov 2019
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods Dylan Slack Sophie Hilgard Emily Jia Sameer Singh Himabindu Lakkaraju FAtt AAML MLAU 79 821 0 06 Nov 2019
Learning Interpretable Models with Causal Guarantees Carolyn Kim Osbert Bastani FaML OOD CML 64 17 0 24 Jan 2019
Learning to Explain with Complemental Examples Atsushi Kanehira Tatsuya Harada 92 40 0 04 Dec 2018
Model Agnostic Supervised Local Explanations Gregory Plumb Denali Molitor Ameet Talwalkar FAtt LRM MILM 129 199 0 09 Jul 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 128 947 0 20 Jun 2018
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation Jianbo Chen Le Song Martin J. Wainwright Michael I. Jordan MLT FAtt 153 575 0 21 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 140 3,970 0 06 Feb 2018
Interpretation of Neural Networks is Fragile Amirata Ghorbani Abubakar Abid James Zou FAtt AAML 133 870 0 29 Oct 2017
Interpreting Blackbox Models via Model Extraction Osbert Bastani Carolyn Kim Hamsa Bastani FAtt 72 173 0 23 May 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 22,018 0 22 May 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 203 3,881 0 10 Apr 2017
Learning Certifiably Optimal Rule Lists for Categorical Data E. Angelino Nicholas Larus-Stone Daniel Alabi Margo Seltzer Cynthia Rudin 97 195 0 06 Apr 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 216 2,905 0 14 Mar 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 193 6,018 0 04 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 405 3,813 0 28 Feb 2017
An unexpected unity among methods for interpreting model predictions Scott M. Lundberg Su-In Lee FAtt 73 110 0 22 Nov 2016
Grad-CAM: Why did you say that? Ramprasaath R. Selvaraju Abhishek Das Ramakrishna Vedantam Michael Cogswell Devi Parikh Dhruv Batra FAtt 70 476 0 22 Nov 2016
Towards Transparent AI Systems: Interpreting Visual Question Answering Models Yash Goyal Akrit Mohapatra Devi Parikh Dhruv Batra 53 74 0 31 Aug 2016
Model-Agnostic Interpretability of Machine Learning Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 86 839 0 16 Jun 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 183 3,706 0 10 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,033 0 16 Feb 2016
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model Benjamin Letham Cynthia Rudin Tyler H. McCormick D. Madigan FAtt 72 743 0 05 Nov 2015
Falling Rule Lists Fulton Wang Cynthia Rudin 58 258 0 21 Nov 2014
How to Explain Individual Classification Decisions D. Baehrens T. Schroeter Stefan Harmeling M. Kawanabe K. Hansen K. Müller FAtt 140 1,104 0 06 Dec 2009