Benchmarking Attribution Methods with Relative Feature Importance

23 July 2019

Papers citing "Benchmarking Attribution Methods with Relative Feature Importance"

34 / 34 papers shown

Title
From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection Moritz Vandenhirtz Julia E. Vogt 38 0 0 09 May 2025
Feature Importance Depends on Properties of the Data: Towards Choosing the Correct Explanations for Your Data and Decision Trees based Models Célia Wafa Ayad Thomas Bonnier Benjamin Bosch Sonali Parbhoo Jesse Read FAtt XAI 103 0 0 11 Feb 2025
Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics Lukas Klein Carsten T. Lüth U. Schlegel Till J. Bungert Mennatallah El-Assady Paul F. Jäger XAI ELM 42 2 0 03 Jan 2025
Explainable AI needs formal notions of explanation correctness Stefan Haufe Rick Wilming Benedict Clark Rustam Zhumagambetov Danny Panknin Ahcène Boubekki XAI 31 1 0 22 Sep 2024
Comprehensive Attribution: Inherently Explainable Vision Model with Feature Detector Xianren Zhang Dongwon Lee Suhang Wang VLM FAtt 45 3 0 27 Jul 2024
Beyond the Veil of Similarity: Quantifying Semantic Continuity in Explainable AI Qi Huang Emanuele Mezzi Osman Mutlu Miltiadis Kofinas Vidya Prasad Shadnan Azwad Khan Elena Ranguelova Niki van Stein 45 0 0 17 Jul 2024
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 61 2 0 25 Apr 2024
3VL: Using Trees to Improve Vision-Language Models' Interpretability Nir Yellinek Leonid Karlinsky Raja Giryes CoGe VLM 49 4 0 28 Dec 2023
Can We Trust Explainable AI Methods on ASR? An Evaluation on Phoneme Recognition Xiao-lan Wu P. Bell A. Rajan 19 5 0 29 May 2023
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus Anna Hedström P. Bommer Kristoffer K. Wickstrom Wojciech Samek Sebastian Lapuschkin Marina M.-C. Höhne 34 21 0 14 Feb 2023
On The Coherence of Quantitative Evaluation of Visual Explanations Benjamin Vandersmissen José Oramas XAI FAtt 34 3 0 14 Feb 2023
Variational Information Pursuit for Interpretable Predictions Aditya Chattopadhyay Kwan Ho Ryan Chan B. Haeffele D. Geman René Vidal DRL 21 10 0 06 Feb 2023
Tracr: Compiled Transformers as a Laboratory for Interpretability David Lindner János Kramár Sebastian Farquhar Matthew Rahtz Tom McGrath Vladimir Mikulik 27 72 0 12 Jan 2023
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability Jonathan Crabbé Alicia Curth Ioana Bica M. Schaar CML 22 16 0 16 Jun 2022
B-cos Networks: Alignment is All We Need for Interpretability Moritz D Boehle Mario Fritz Bernt Schiele 39 84 0 20 May 2022
Clinical outcome prediction under hypothetical interventions -- a representation learning framework for counterfactual reasoning Yikuan Li M. Mamouei Shishir Rao A. Hassaine D. Canoy Thomas Lukasiewicz K. Rahimi G. Salimi-Khorshidi OOD CML AI4CE 28 1 0 15 May 2022
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset Leon Sixt M. Schuessler Oana-Iuliana Popescu Philipp Weiß Tim Landgraf FAtt 26 14 0 25 Apr 2022
Human-Centered Concept Explanations for Neural Networks Chih-Kuan Yeh Been Kim Pradeep Ravikumar FAtt 34 25 0 25 Feb 2022
Evaluating Feature Attribution Methods in the Image Domain Arne Gevaert Axel-Jan Rousseau Thijs Becker D. Valkenborg T. D. Bie Yvan Saeys FAtt 21 22 0 22 Feb 2022
Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review F. Giuste Wenqi Shi Yuanda Zhu Tarun Naren Monica Isgut Ying Sha L. Tong Mitali S. Gupte May D. Wang 21 73 0 23 Dec 2021
Evaluating saliency methods on artificial data with different background types Céline Budding Fabian Eitel K. Ritter Stefan Haufe XAI FAtt MedIm 24 5 0 09 Dec 2021
HIVE: Evaluating the Human Interpretability of Visual Explanations Sunnie S. Y. Kim Nicole Meister V. V. Ramaswamy Ruth C. Fong Olga Russakovsky 66 114 0 06 Dec 2021
Self-Interpretable Model with TransformationEquivariant Interpretation Yipei Wang Xiaoqian Wang 38 23 0 09 Nov 2021
A Survey on the Robustness of Feature Importance and Counterfactual Explanations Saumitra Mishra Sanghamitra Dutta Jason Long Daniele Magazzeni AAML 14 58 0 30 Oct 2021
Longitudinal Distance: Towards Accountable Instance Attribution Rosina O. Weber Prateek Goel S. Amiri G. Simpson 16 0 0 23 Aug 2021
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning Yang Liu Sujay Khandagale Colin White W. Neiswanger 37 65 0 23 Jun 2021
Do Feature Attribution Methods Correctly Attribute Features? Yilun Zhou Serena Booth Marco Tulio Ribeiro J. Shah FAtt XAI 24 132 0 27 Apr 2021
Improving Attribution Methods by Learning Submodular Functions Piyushi Manupriya Tarun Ram Menta S. Jagarlapudi V. Balasubramanian TDI 22 6 0 19 Apr 2021
Do Input Gradients Highlight Discriminative Features? Harshay Shah Prateek Jain Praneeth Netrapalli AAML FAtt 21 57 0 25 Feb 2021
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 29 821 0 16 Sep 2020
Evaluating and Aggregating Feature-based Model Explanations Umang Bhatt Adrian Weller J. M. F. Moura XAI 33 218 0 01 May 2020
Measuring and improving the quality of visual explanations Agnieszka Grabska-Barwiñska XAI FAtt 14 3 0 14 Mar 2020
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan Ö. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 122 297 0 17 Oct 2019
Explaining Anomalies Detected by Autoencoders Using SHAP Liat Antwarg Ronnie Mindlin Miller Bracha Shapira Lior Rokach FAtt TDI 11 86 0 06 Mar 2019