Nothing Else Matters: Model-Agnostic Explanations By Identifying
Prediction Invariance

Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance

17 November 2016

Marco Tulio Ribeiro

Carlos Guestrin

Papers citing "Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance"

15 / 15 papers shown

Title
neuralGAM: An R Package for Fitting Generalized Additive Neural Networks Ines Ortega-Fernandez Marta Sestelo 41 0 0 13 May 2025
Recent Advances in Malware Detection: Graph Learning and Explainability Hossein Shokouhinejad Roozbeh Razavi-Far Hesamodin Mohammadian Mahdi Rabbani Samuel Ansong Griffin Higgins Ali Ghorbani AAML 83 2 0 14 Feb 2025
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients Evandro S. Ortigossa Fábio F. Dias Brian Barr Claudio T. Silva L. G. Nonato FAtt 66 2 0 25 Apr 2024
Comparison of decision trees with Local Interpretable Model-Agnostic Explanations (LIME) technique and multi-linear regression for explaining support vector regression model in terms of root mean square error (RMSE) values Amit Thombre FAtt 21 1 0 10 Apr 2024
Ensemble Interpretation: A Unified Method for Interpretable Machine Learning Chao Min Guoyong Liao Guo-quan Wen Yingjun Li Xing Guo FAtt 19 0 0 11 Dec 2023
To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods E. Amparore Alan Perotti P. Bajardi FAtt 33 68 0 01 Jun 2021
Why model why? Assessing the strengths and limitations of LIME Jurgen Dieber S. Kirrane FAtt 26 97 0 30 Nov 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 52 371 0 30 Apr 2020
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 44 6,125 0 22 Oct 2019
Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods Oana-Maria Camburu Eleonora Giunchiglia Jakob N. Foerster Thomas Lukasiewicz Phil Blunsom FAtt AAML 34 60 0 04 Oct 2019
Understanding Individual Decisions of CNNs via Contrastive Backpropagation Jindong Gu Yinchong Yang Volker Tresp FAtt 17 94 0 05 Dec 2018
Did the Model Understand the Question? Pramod Kaushik Mudrakarta Ankur Taly Mukund Sundararajan Kedar Dhamdhere ELM OOD FAtt 27 196 0 14 May 2018
A Symbolic Approach to Explaining Bayesian Network Classifiers Andy Shih Arthur Choi Adnan Darwiche FAtt 27 237 0 09 May 2018
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges Gabrielle Ras Marcel van Gerven W. Haselager XAI 17 217 0 20 Mar 2018
Interpretable Deep Convolutional Neural Networks via Meta-learning Xuan Liu Xiaoguang Wang Stan Matwin FaML 27 38 0 02 Feb 2018