Title
Axiomatic Explainer Globalness via Optimal Transport Davin Hill Josh Bone A. Masoomi Max Torop Jennifer Dy 170 1 0 13 Mar 2025
DepressionX: Knowledge Infused Residual Attention for Explainable Depression Severity Assessment Yusif Ibrahimov Tarique Anwar Tommy Yuan 153 0 0 28 Jan 2025
XEQ Scale for Evaluating XAI Experience Quality A. Wijekoon Nirmalie Wiratunga D. Corsar Kyle Martin Ikechukwu Nkisi-Orji Belén Díaz-Agudo Derek Bridge 117 2 0 20 Jan 2025
DILA: Dictionary Label Attention for Mechanistic Interpretability in High-dimensional Multi-label Medical Coding Prediction John Wu David Wu Jimeng Sun 297 0 0 16 Sep 2024
Normalized AOPC: Fixing Misleading Faithfulness Metrics for Feature Attribution Explainability Joakim Edin Andreas Geert Motzfeldt Casper L. Christensen Tuukka Ruotsalo Lars Maaløe Maria Maistro 102 4 0 15 Aug 2024
CELL your Model: Contrastive Explanations for Large Language Models Ronny Luss Erik Miehling Amit Dhurandhar 95 0 0 17 Jun 2024
SurvLIME: A method for explaining machine learning survival models M. Kovalev Lev V. Utkin E. Kasimov 239 90 0 18 Mar 2020
Generalized Linear Rule Models Dennis L. Wei S. Dash Tian Gao Oktay Gunluk 48 63 0 05 Jun 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 53 110 0 12 Nov 2018
Improving Simple Models with Confidence Profiles Amit Dhurandhar Karthikeyan Shanmugam Ronny Luss Peder Olsen 51 46 0 19 Jul 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 122 940 0 20 Jun 2018
Boolean Decision Rules via Column Generation S. Dash Oktay Gunluk Dennis L. Wei 63 174 0 24 May 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 107 589 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 124 3,954 0 06 Feb 2018
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 207 1,837 0 30 Nov 2017
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations Abhishek Kumar P. Sattigeri Avinash Balakrishnan BDL DRL 78 523 0 02 Nov 2017
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 98 2,350 0 01 Nov 2017
Interpretable Convolutional Neural Networks Quanshi Zhang Ying Nian Wu Song-Chun Zhu FAtt 64 780 0 02 Oct 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 239 4,259 0 22 Jun 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 169 2,882 0 14 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 382 3,776 0 28 Feb 2017
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 268 19,929 0 07 Oct 2016
Rationalizing Neural Predictions Tao Lei Regina Barzilay Tommi Jaakkola 108 812 0 13 Jun 2016
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Xi Chen Yan Duan Rein Houthooft John Schulman Ilya Sutskever Pieter Abbeel GAN 157 4,232 0 12 Jun 2016
Generating Visual Explanations Lisa Anne Hendricks Zeynep Akata Marcus Rohrbach Jeff Donahue Bernt Schiele Trevor Darrell VLM FAtt 81 618 0 28 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.1K 16,931 0 16 Feb 2016
Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks Anh Totti Nguyen J. Yosinski Jeff Clune 54 329 0 11 Feb 2016
Distilling the Knowledge in a Neural Network Geoffrey E. Hinton Oriol Vinyals J. Dean FedML 327 19,609 0 09 Mar 2015
Do Deep Nets Really Need to be Deep? Lei Jimmy Ba R. Caruana 160 2,117 0 21 Dec 2013
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 301 7,289 0 20 Dec 2013