Neural Network Attributions: A Causal Perspective

6 February 2019

Papers citing "Neural Network Attributions: A Causal Perspective"

31 / 31 papers shown

Title
Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning Numair Sani Daniel Malinsky I. Shpitser CML 79 15 0 10 Jan 2025
On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach Ruichu Cai Yuxuan Zhu Xuexin Chen Yuan Fang Min-man Wu Jie Qiao Zhifeng Hao 54 7 0 31 Dec 2024
LLMScan: Causal Scan for LLM Misbehavior Detection Mengdi Zhang Kai Kiat Goh Peixin Zhang Jun Sun Rose Lin Xin Hongyu Zhang 25 0 0 22 Oct 2024
Robust Ranking Explanations Chao Chen Chenghua Guo Guixiang Ma Ming Zeng Xi Zhang Sihong Xie FAtt AAML 35 0 0 08 Jul 2023
Causal Analysis for Robust Interpretability of Neural Networks Ola Ahmad Nicolas Béreux Loïc Baret V. Hashemi Freddy Lecue CML 29 3 0 15 May 2023
Towards Learning and Explaining Indirect Causal Effects in Neural Networks Abbaavaram Gowtham Reddy Saketh Bachu Harsh Nilesh Pathak Ben Godfrey V. Balasubramanian V. Varshaneya Satya Narayanan Kar CML 31 0 0 24 Mar 2023
SUNY: A Visual Interpretation Framework for Convolutional Neural Networks from a Necessary and Sufficient Perspective Xiwei Xuan Ziquan Deng Hsuan-Tien Lin Z. Kong Kwan-Liu Ma AAML FAtt 35 2 0 01 Mar 2023
Variational Information Pursuit for Interpretable Predictions Aditya Chattopadhyay Kwan Ho Ryan Chan B. Haeffele D. Geman René Vidal DRL 24 10 0 06 Feb 2023
On the Robustness of Explanations of Deep Neural Network Models: A Survey Amlan Jyoti Karthik Balaji Ganesh Manoj Gayala Nandita Lakshmi Tunuguntla Sandesh Kamath V. Balasubramanian XAI FAtt AAML 32 4 0 09 Nov 2022
Causal Explanation for Reinforcement Learning: Quantifying State and Temporal Importance Xiaoxiao Wang Fanyu Meng Xin Liu Z. Kong Xin Chen XAI CML FAtt 39 4 0 24 Oct 2022
Adaptive Fairness Improvement Based on Causality Analysis Mengdi Zhang Jun Sun 24 31 0 15 Sep 2022
Leveraging Explanations in Interactive Machine Learning: An Overview Stefano Teso Öznur Alkan Wolfgang Stammer Elizabeth M. Daly XAI FAtt LRM 26 62 0 29 Jul 2022
A Causal Lens for Controllable Text Generation Zhiting Hu Erran L. Li 45 59 0 22 Jan 2022
On Causally Disentangled Representations Abbavaram Gowtham Reddy Benin Godfrey L V. Balasubramanian OOD CML 34 21 0 10 Dec 2021
Inducing Causal Structure for Interpretable Neural Networks Atticus Geiger Zhengxuan Wu Hanson Lu J. Rozner Elisa Kreiss Thomas Icard Noah D. Goodman Christopher Potts CML OOD 35 70 0 01 Dec 2021
Matching Learned Causal Effects of Neural Networks with Domain Priors Sai Srinivas Kancheti Abbavaram Gowtham Reddy V. Balasubramanian Amit Sharma CML 33 12 0 24 Nov 2021
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
Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems Ziyuan Zhong Zhisheng Hu Shengjian Guo Xinyang Zhang Zhenyu Zhong Baishakhi Ray AAML 28 23 0 14 Sep 2021
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis Bas H. M. van der Velden Hugo J. Kuijf K. Gilhuijs M. Viergever XAI 35 638 0 22 Jul 2021
Causal Abstractions of Neural Networks Atticus Geiger Hanson Lu Thomas Icard Christopher Potts NAI CML 17 218 0 06 Jun 2021
Did I do that? Blame as a means to identify controlled effects in reinforcement learning Oriol Corcoll Youssef Mohamed Raul Vicente 18 3 0 01 Jun 2021
Causal Learning for Socially Responsible AI Lu Cheng Ahmadreza Mosallanezhad Paras Sheth Huan Liu 71 13 0 25 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
Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things Jing Zhang Dacheng Tao 36 462 0 17 Nov 2020
Generative causal explanations of black-box classifiers Matthew R. O’Shaughnessy Gregory H. Canal Marissa Connor Mark A. Davenport Christopher Rozell CML 30 73 0 24 Jun 2020
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation Raha Moraffah Mansooreh Karami Ruocheng Guo A. Raglin Huan Liu CML ELM XAI 27 213 0 09 Mar 2020
Biophysical models of cis-regulation as interpretable neural networks Ammar Tareen J. Kinney AI4CE 15 22 0 30 Dec 2019
Feature relevance quantification in explainable AI: A causal problem Dominik Janzing Lenon Minorics Patrick Blobaum FAtt CML 13 278 0 29 Oct 2019
CXPlain: Causal Explanations for Model Interpretation under Uncertainty Patrick Schwab W. Karlen FAtt CML 34 205 0 27 Oct 2019
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning Fan Yang Mengnan Du Xia Hu XAI ELM 27 66 0 16 Jul 2019
A causal framework for explaining the predictions of black-box sequence-to-sequence models David Alvarez-Melis Tommi Jaakkola CML 232 200 0 06 Jul 2017