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T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients

25 April 2024

Claudio T. Silva

Papers citing "T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients"

41 / 41 papers shown

Title
OpenXAI: Towards a Transparent Evaluation of Model Explanations Chirag Agarwal Dan Ley Satyapriya Krishna Eshika Saxena Martin Pawelczyk Nari Johnson Isha Puri Marinka Zitnik Himabindu Lakkaraju XAI 71 147 0 22 Jun 2022
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations Jessica Dai Sohini Upadhyay Ulrich Aïvodji Stephen H. Bach Himabindu Lakkaraju 72 57 0 15 May 2022
Rethinking Stability for Attribution-based Explanations Chirag Agarwal Nari Johnson Martin Pawelczyk Satyapriya Krishna Eshika Saxena Marinka Zitnik Himabindu Lakkaraju FAtt 74 53 0 14 Mar 2022
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective Satyapriya Krishna Tessa Han Alex Gu Steven Wu S. Jabbari Himabindu Lakkaraju 246 195 0 03 Feb 2022
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI Meike Nauta Jan Trienes Shreyasi Pathak Elisa Nguyen Michelle Peters Yasmin Schmitt Jorg Schlotterer M. V. Keulen C. Seifert ELM XAI 127 418 0 20 Jan 2022
A Survey on Methods and Metrics for the Assessment of Explainability under the Proposed AI Act Francesco Sovrano Salvatore Sapienza M. Palmirani F. Vitali 48 18 0 21 Oct 2021
Deep Neural Networks and Tabular Data: A Survey V. Borisov Tobias Leemann Kathrin Seßler Johannes Haug Martin Pawelczyk Gjergji Kasneci LMTD 112 696 0 05 Oct 2021
To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods E. Amparore Alan Perotti P. Bajardi FAtt 62 67 0 01 Jun 2021
Explaining a Series of Models by Propagating Shapley Values Hugh Chen Scott M. Lundberg Su-In Lee TDI FAtt 83 129 0 30 Apr 2021
Benchmarking and Survey of Explanation Methods for Black Box Models F. Bodria F. Giannotti Riccardo Guidotti Francesca Naretto D. Pedreschi S. Rinzivillo XAI 101 229 0 25 Feb 2021
Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression Ian Covert Su-In Lee FAtt 56 171 0 02 Dec 2020
Robust and Stable Black Box Explanations Himabindu Lakkaraju Nino Arsov Osbert Bastani AAML FAtt 58 84 0 12 Nov 2020
Feature Importance Ranking for Deep Learning Maksymilian Wojtas Ke Chen 186 118 0 18 Oct 2020
Principles and Practice of Explainable Machine Learning Vaishak Belle I. Papantonis FaML 67 449 0 18 Sep 2020
Captum: A unified and generic model interpretability library for PyTorch Narine Kokhlikyan Vivek Miglani Miguel Martin Edward Wang B. Alsallakh ... Alexander Melnikov Natalia Kliushkina Carlos Araya Siqi Yan Orion Reblitz-Richardson FAtt 144 846 0 16 Sep 2020
Explaining Models by Propagating Shapley Values of Local Components Hugh Chen Scott M. Lundberg Su-In Lee FAtt FedML 60 109 0 27 Nov 2019
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 130 6,293 0 22 Oct 2019
The many Shapley values for model explanation Mukund Sundararajan A. Najmi TDI FAtt 64 635 0 22 Aug 2019
Benchmarking Attribution Methods with Relative Feature Importance Mengjiao Yang Been Kim FAtt XAI 69 142 0 23 Jul 2019
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI Erico Tjoa Cuntai Guan XAI 112 1,451 0 17 Jul 2019
A Human-Grounded Evaluation of SHAP for Alert Processing Hilde J. P. Weerts Werner van Ipenburg Mykola Pechenizkiy FAtt 55 70 0 07 Jul 2019
Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance Giles Hooker L. Mentch Siyu Zhou 82 158 0 01 May 2019
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values K. Aas Martin Jullum Anders Løland FAtt TDI 76 624 0 25 Mar 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin S. Wäldchen Alexander Binder G. Montavon Wojciech Samek K. Müller 102 1,017 0 26 Feb 2019
Beyond One-hot Encoding: lower dimensional target embedding Pau Rodríguez López Miguel Angel Bautista Jordi Gonzalez Sergio Escalera 44 343 0 28 Jun 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 84 528 0 21 Jun 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 128 947 0 20 Jun 2018
RISE: Randomized Input Sampling for Explanation of Black-box Models Vitali Petsiuk Abir Das Kate Saenko FAtt 181 1,176 0 19 Jun 2018
Visualizing the Feature Importance for Black Box Models Giuseppe Casalicchio Christoph Molnar B. Bischl FAtt 38 183 0 18 Apr 2018
Consistent Individualized Feature Attribution for Tree Ensembles Scott M. Lundberg G. Erion Su-In Lee FAtt TDI 66 1,405 0 12 Feb 2018
Considerations When Learning Additive Explanations for Black-Box Models S. Tan Giles Hooker Paul Koch Albert Gordo R. Caruana FAtt 60 24 0 26 Jan 2018
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
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 193 6,018 0 04 Mar 2017
Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt 52 64 0 17 Nov 2016
Model-Agnostic Interpretability of Machine Learning Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 86 839 0 16 Jun 2016
Not Just a Black Box: Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Shcherbina A. Kundaje FAtt 87 791 0 05 May 2016
XGBoost: A Scalable Tree Boosting System Tianqi Chen Carlos Guestrin 814 39,062 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 17,033 0 16 Feb 2016
OpenML: networked science in machine learning Joaquin Vanschoren Jan N. van Rijn B. Bischl Luís Torgo FedML AI4CE 167 1,327 0 29 Jul 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 314 7,316 0 20 Dec 2013