Explaining Visual Models by Causal Attribution

19 September 2019

Papers citing "Explaining Visual Models by Causal Attribution"

22 / 22 papers shown

Title
Counterfactual Explanations for Deep Learning-Based Traffic Forecasting Rushan Wang Yanan Xin Yatao Zhang Fernando Pérez-Cruz Martin Raubal AI4TS 24 3 0 01 May 2024
Causal Feature Selection for Responsible Machine Learning Raha Moraffah Paras Sheth Saketh Vishnubhatla Huan Liu CML 27 2 0 05 Feb 2024
Causal Generative Explainers using Counterfactual Inference: A Case Study on the Morpho-MNIST Dataset William Taylor-Melanson Zahra Sadeghi Stan Matwin CML 19 5 0 21 Jan 2024
Provable Robust Saliency-based Explanations Chao Chen Chenghua Guo Guixiang Ma Ming Zeng Xi Zhang Sihong Xie AAML FAtt 20 0 0 28 Dec 2022
Deep Causal Learning for Robotic Intelligence Y. Li CML 30 5 0 23 Dec 2022
Causality-Aware Local Interpretable Model-Agnostic Explanations Martina Cinquini Riccardo Guidotti CML 44 1 0 10 Dec 2022
Deep Causal Learning: Representation, Discovery and Inference Zizhen Deng Xiaolong Zheng Hu Tian D. Zeng CML BDL 28 11 0 07 Nov 2022
Deep Structural Causal Shape Models Rajat Rasal Daniel Coelho De Castro Nick Pawlowski Ben Glocker 3DV MedIm 28 12 0 23 Aug 2022
Explanatory causal effects for model agnostic explanations Jiuyong Li Ha Xuan Tran T. Le Lin Liu Kui Yu Jixue Liu CML 22 1 0 23 Jun 2022
Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces Kamran Alipour Aditya Lahiri Ehsan Adeli Babak Salimi M. Pazzani CML 20 7 0 10 Jun 2022
Causality-based Neural Network Repair Bing-Jie Sun Jun Sun Hong Long Pham Jie Shi 19 78 0 20 Apr 2022
VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queries Pablo Sánchez-Martín Miriam Rateike Isabel Valera CML BDL 23 14 0 27 Oct 2021
Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation Thien Q. Tran Kazuto Fukuchi Youhei Akimoto Jun Sakuma CML 32 10 0 09 Sep 2021
Causal Learning for Socially Responsible AI Lu Cheng Ahmadreza Mosallanezhad Paras Sheth Huan Liu 71 13 0 25 Apr 2021
Explaining the Black-box Smoothly- A Counterfactual Approach Junyu Chen Yong Du Yufan He W. Paul Segars Ye Li MedIm FAtt 63 83 0 11 Jan 2021
Socially Responsible AI Algorithms: Issues, Purposes, and Challenges Lu Cheng Kush R. Varshney Huan Liu FaML 20 145 0 01 Jan 2021
Generative causal explanations of black-box classifiers Matthew R. O’Shaughnessy Gregory H. Canal Marissa Connor Mark A. Davenport Christopher Rozell CML 17 73 0 24 Jun 2020
Causal Inference with Deep Causal Graphs Álvaro Parafita Jordi Vitrià CML 6 10 0 15 Jun 2020
Deep Structural Causal Models for Tractable Counterfactual Inference Nick Pawlowski Daniel Coelho De Castro Ben Glocker CML MedIm 25 229 0 11 Jun 2020
Adversarial Robustness on In- and Out-Distribution Improves Explainability Maximilian Augustin Alexander Meinke Matthias Hein OOD 62 98 0 20 Mar 2020
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation Raha Moraffah Mansooreh Karami Ruocheng Guo A. Raglin Huan Liu CML ELM XAI 18 212 0 09 Mar 2020
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers Divyat Mahajan Chenhao Tan Amit Sharma OOD CML 17 205 0 06 Dec 2019