Benchmarking and Survey of Explanation Methods for Black Box Models

25 February 2021

Papers citing "Benchmarking and Survey of Explanation Methods for Black Box Models"

50 / 105 papers shown

Title
Improving performance of deep learning models with axiomatic attribution priors and expected gradients G. Erion Joseph D. Janizek Pascal Sturmfels Scott M. Lundberg Su-In Lee OOD BDL FAtt 45 81 0 25 Jun 2019
DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems Muhammad Rehman Zafar N. Khan FAtt 80 154 0 24 Jun 2019
LioNets: Local Interpretation of Neural Networks through Penultimate Layer Decoding Ioannis Mollas Nikolaos Bassiliades Grigorios Tsoumakas 27 12 0 15 Jun 2019
XRAI: Better Attributions Through Regions A. Kapishnikov Tolga Bolukbasi Fernanda Viégas Michael Terry FAtt XAI 52 212 0 06 Jun 2019
Explain Yourself! Leveraging Language Models for Commonsense Reasoning Nazneen Rajani Bryan McCann Caiming Xiong R. Socher ReLM LRM 66 561 0 06 Jun 2019
Concept Tree: High-Level Representation of Variables for More Interpretable Surrogate Decision Trees X. Renard Nicolas Woloszko Jonathan Aigrain Marcin Detyniecki 20 11 0 04 Jun 2019
Leveraging Latent Features for Local Explanations Ronny Luss Pin-Yu Chen Amit Dhurandhar P. Sattigeri Yunfeng Zhang Karthikeyan Shanmugam Chun-Chen Tu FAtt 75 38 0 29 May 2019
Model-Agnostic Counterfactual Explanations for Consequential Decisions Amir-Hossein Karimi Gilles Barthe Borja Balle Isabel Valera 88 320 0 27 May 2019
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations R. Mothilal Amit Sharma Chenhao Tan CML 104 1,005 0 19 May 2019
Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications Pouya Pezeshkpour Yifan Tian Sameer Singh KELM AAML 74 73 0 02 May 2019
Attention is not Explanation Sarthak Jain Byron C. Wallace FAtt 89 1,307 0 26 Feb 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin S. Wäldchen Alexander Binder G. Montavon Wojciech Samek K. Müller 78 1,005 0 26 Feb 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin Yu XAI HAI 138 1,428 0 14 Jan 2019
A Comprehensive Survey on Graph Neural Networks Zonghan Wu Shirui Pan Fengwen Chen Guodong Long Chengqi Zhang Philip S. Yu FaML GNN AI4TS AI4CE 415 8,441 0 03 Jan 2019
TED: Teaching AI to Explain its Decisions Michael Hind Dennis L. Wei Murray Campbell Noel Codella Amit Dhurandhar Aleksandra Mojsilović Karthikeyan N. Ramamurthy Kush R. Varshney 41 110 0 12 Nov 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 123 1,947 0 08 Oct 2018
Local Interpretable Model-agnostic Explanations of Bayesian Predictive Models via Kullback-Leibler Projections Tomi Peltola FAtt BDL 45 39 0 05 Oct 2018
RuleMatrix: Visualizing and Understanding Classifiers with Rules Yao Ming Huamin Qu E. Bertini FAtt 56 215 0 17 Jul 2018
Model Agnostic Supervised Local Explanations Gregory Plumb Denali Molitor Ameet Talwalkar FAtt LRM MILM 90 196 0 09 Jul 2018
This Looks Like That: Deep Learning for Interpretable Image Recognition Chaofan Chen Oscar Li Chaofan Tao A. Barnett Jonathan Su Cynthia Rudin 191 1,172 0 27 Jun 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 105 938 0 20 Jun 2018
RISE: Randomized Input Sampling for Explanation of Black-box Models Vitali Petsiuk Abir Das Kate Saenko FAtt 161 1,164 0 19 Jun 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 75 1,849 0 31 May 2018
Local Rule-Based Explanations of Black Box Decision Systems Riccardo Guidotti A. Monreale Salvatore Ruggieri D. Pedreschi Franco Turini F. Giannotti 118 436 0 28 May 2018
Boolean Decision Rules via Column Generation S. Dash Oktay Gunluk Dennis L. Wei 49 174 0 24 May 2018
Explainable Recommendation: A Survey and New Perspectives Yongfeng Zhang Xu Chen XAI LRM 77 870 0 30 Apr 2018
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation Jianbo Chen Le Song Martin J. Wainwright Michael I. Jordan MLT FAtt 127 568 0 21 Feb 2018
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives Amit Dhurandhar Pin-Yu Chen Ronny Luss Chun-Chen Tu Pai-Shun Ting Karthikeyan Shanmugam Payel Das FAtt 96 587 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 92 3,922 0 06 Feb 2018
Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation Heyi Li Yunke Tian Klaus Mueller Xin Chen FAtt 56 39 0 22 Dec 2017
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 172 1,828 0 30 Nov 2017
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 88 2,332 0 01 Nov 2017
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks Aditya Chattopadhyay Anirban Sarkar Prantik Howlader V. Balasubramanian FAtt 93 2,280 0 30 Oct 2017
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions Oscar Li Hao Liu Chaofan Chen Cynthia Rudin 139 586 0 13 Oct 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 232 4,229 0 22 Jun 2017
Explaining Recurrent Neural Network Predictions in Sentiment Analysis L. Arras G. Montavon K. Müller Wojciech Samek FAtt 50 354 0 22 Jun 2017
SmoothGrad: removing noise by adding noise D. Smilkov Nikhil Thorat Been Kim F. Viégas Martin Wattenberg FAtt ODL 196 2,215 0 12 Jun 2017
Attention Is All You Need Ashish Vaswani Noam M. Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan Gomez Lukasz Kaiser Illia Polosukhin 3DV 514 129,831 0 12 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 688 21,613 0 22 May 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 138 3,848 0 10 Apr 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 152 2,854 0 14 Mar 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 147 5,920 0 04 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 354 3,742 0 28 Feb 2017
Understanding Neural Networks through Representation Erasure Jiwei Li Will Monroe Dan Jurafsky AAML MILM 81 562 0 24 Dec 2016
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 293 2,098 0 24 Oct 2016
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 232 19,796 0 07 Oct 2016
Semi-Supervised Classification with Graph Convolutional Networks Thomas Kipf Max Welling GNN SSL 536 28,901 0 09 Sep 2016
Tutorial on Variational Autoencoders Carl Doersch BDL DRL 85 1,736 0 19 Jun 2016
Scalable Bayesian Rule Lists Hongyu Yang Cynthia Rudin Margo Seltzer TPM 37 211 0 27 Feb 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 746 16,828 0 16 Feb 2016