On the (In)fidelity and Sensitivity for Explanations

On the (In)fidelity and Sensitivity for Explanations

27 January 2019

David I. Inouye

Pradeep Ravikumar

Papers citing "On the (In)fidelity and Sensitivity for Explanations"

19 / 69 papers shown

Title
Quantifying Explainability in NLP and Analyzing Algorithms for Performance-Explainability Tradeoff Michael J. Naylor C. French Samantha R. Terker Uday Kamath 30 10 0 12 Jul 2021
What will it take to generate fairness-preserving explanations? Jessica Dai Sohini Upadhyay Stephen H. Bach Himabindu Lakkaraju FAtt FaML 13 14 0 24 Jun 2021
On Locality of Local Explanation Models Sahra Ghalebikesabi Lucile Ter-Minassian Karla Diaz-Ordaz Chris Holmes FedML FAtt 13 39 0 24 Jun 2021
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning Yang Liu Sujay Khandagale Colin White W. Neiswanger 26 65 0 23 Jun 2021
Shapley Explanation Networks Rui Wang Xiaoqian Wang David I. Inouye TDI FAtt 17 44 0 06 Apr 2021
Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing Ioannis Kakogeorgiou Konstantinos Karantzalos XAI 21 118 0 03 Apr 2021
Robust Models Are More Interpretable Because Attributions Look Normal Zifan Wang Matt Fredrikson Anupam Datta OOD FAtt 28 25 0 20 Mar 2021
EX-RAY: Distinguishing Injected Backdoor from Natural Features in Neural Networks by Examining Differential Feature Symmetry Yingqi Liu Guangyu Shen Guanhong Tao Zhenting Wang Shiqing Ma X. Zhang AAML 19 8 0 16 Mar 2021
Do Input Gradients Highlight Discriminative Features? Harshay Shah Prateek Jain Praneeth Netrapalli AAML FAtt 18 57 0 25 Feb 2021
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors Yi-Shan Lin Wen-Chuan Lee Z. Berkay Celik XAI 24 93 0 22 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 6 821 0 16 Sep 2020
A simple defense against adversarial attacks on heatmap explanations Laura Rieger Lars Kai Hansen FAtt AAML 25 37 0 13 Jul 2020
Proper Network Interpretability Helps Adversarial Robustness in Classification Akhilan Boopathy Sijia Liu Gaoyuan Zhang Cynthia Liu Pin-Yu Chen Shiyu Chang Luca Daniel AAML FAtt 8 66 0 26 Jun 2020
Adversarial Infidelity Learning for Model Interpretation Jian Liang Bing Bai Yuren Cao Kun Bai Fei-Yue Wang AAML 36 18 0 09 Jun 2020
Evaluating and Aggregating Feature-based Model Explanations Umang Bhatt Adrian Weller J. M. F. Moura XAI 28 218 0 01 May 2020
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks Joseph D. Janizek Pascal Sturmfels Su-In Lee FAtt 20 143 0 10 Feb 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
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning Fan Yang Mengnan Du Xia Hu XAI ELM 19 66 0 16 Jul 2019
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,235 0 24 Jun 2017