The Out-of-Distribution Problem in Explainability and Search Methods for
Feature Importance Explanations

The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations

1 June 2021

Joey Tianyi Zhou

Papers citing "The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations"

14 / 64 papers shown

Title
On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective M. Serrurier Franck Mamalet Thomas Fel Louis Bethune Thibaut Boissin AAML FAtt 32 4 0 14 Jun 2022
Mediators: Conversational Agents Explaining NLP Model Behavior Nils Feldhus A. Ravichandran Sebastian Möller 43 16 0 13 Jun 2022
Order-sensitive Shapley Values for Evaluating Conceptual Soundness of NLP Models Kaiji Lu Anupam Datta 21 0 0 01 Jun 2022
A Sea of Words: An In-Depth Analysis of Anchors for Text Data Gianluigi Lopardo F. Precioso Damien Garreau 27 6 0 27 May 2022
Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection Esma Balkir I. Nejadgholi Kathleen C. Fraser S. Kiritchenko FAtt 41 27 0 06 May 2022
Text Transformations in Contrastive Self-Supervised Learning: A Review Amrita Bhattacharjee Mansooreh Karami Huan Liu SSL 29 23 0 22 Mar 2022
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis Thomas Fel Mélanie Ducoffe David Vigouroux Rémi Cadène Mikael Capelle C. Nicodeme Thomas Serre AAML 26 41 0 15 Feb 2022
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond Anna Hedström Leander Weber Dilyara Bareeva Daniel G. Krakowczyk Franz Motzkus Wojciech Samek Sebastian Lapuschkin Marina M.-C. Höhne XAI ELM 21 168 0 14 Feb 2022
Framework for Evaluating Faithfulness of Local Explanations S. Dasgupta Nave Frost Michal Moshkovitz FAtt 116 61 0 01 Feb 2022
Rethinking Attention-Model Explainability through Faithfulness Violation Test Y. Liu Haoliang Li Yangyang Guo Chen Kong Jing Li Shiqi Wang FAtt 121 43 0 28 Jan 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 28 396 0 20 Jan 2022
Double Trouble: How to not explain a text classifier's decisions using counterfactuals synthesized by masked language models? Thang M. Pham Trung H. Bui Long Mai Anh Totti Nguyen 21 7 0 22 Oct 2021
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations N. Jethani Mukund Sudarshan Yindalon Aphinyanagphongs Rajesh Ranganath FAtt 82 69 0 02 Mar 2021
Feature Importance Ranking for Deep Learning Maksymilian Wojtas Ke Chen 144 115 0 18 Oct 2020