CXPlain: Causal Explanations for Model Interpretation under Uncertainty

27 October 2019

Papers citing "CXPlain: Causal Explanations for Model Interpretation under Uncertainty"

50 / 108 papers shown

Title
Causality for Inherently Explainable Transformers: CAT-XPLAIN Subash Khanal Benjamin Brodie Xin Xing Ai-Ling Lin Nathan Jacobs 113 4 0 29 Jun 2022
Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions Zulqarnain Khan Davin Hill A. Masoomi Joshua Bone Jennifer Dy AAML 138 4 0 24 Jun 2022
Explanatory causal effects for model agnostic explanations Jiuyong Li Ha Xuan Tran T. Le Lin Liu Kui Yu Jixue Liu CML 80 1 0 23 Jun 2022
The Manifold Hypothesis for Gradient-Based Explanations Sebastian Bordt Uddeshya Upadhyay Zeynep Akata U. V. Luxburg FAtt AAML 59 14 0 15 Jun 2022
Machines Explaining Linear Programs David Steinmann Matej Zečević Devendra Singh Dhami Kristian Kersting FAtt 26 0 0 14 Jun 2022
Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces Kamran Alipour Aditya Lahiri Ehsan Adeli Babak Salimi M. Pazzani CML 76 7 0 10 Jun 2022
On the Bias-Variance Characteristics of LIME and SHAP in High Sparsity Movie Recommendation Explanation Tasks Claudia V. Roberts Ehtsham Elahi Ashok Chandrashekar FAtt 53 4 0 09 Jun 2022
Differentiable Invariant Causal Discovery Yu Wang An Zhang Xiang Wang Yancheng Yuan Xiangnan He Tat-Seng Chua OOD CML 143 2 0 31 May 2022
Explaining Causal Models with Argumentation: the Case of Bi-variate Reinforcement Antonio Rago P. Baroni Francesca Toni CML 73 5 0 23 May 2022
Towards Explanation for Unsupervised Graph-Level Representation Learning Qinghua Zheng Jihong Wang Minnan Luo Yaoliang Yu Jundong Li L. Yao Xiao Chang 58 1 0 20 May 2022
Trustworthy Graph Neural Networks: Aspects, Methods and Trends He Zhang Bang Wu Lizhen Qu Shirui Pan Hanghang Tong Jian Pei 139 110 0 16 May 2022
It Takes Two Flints to Make a Fire: Multitask Learning of Neural Relation and Explanation Classifiers Zheng Tang Mihai Surdeanu 136 6 0 25 Apr 2022
Reinforced Causal Explainer for Graph Neural Networks Xiang Wang Y. Wu An Zhang Fuli Feng Xiangnan He Tat-Seng Chua CML 126 47 0 23 Apr 2022
Deconfounding to Explanation Evaluation in Graph Neural Networks Yingmin Wu Xiang Wang An Zhang Helen Zhou Fuli Feng Xiangnan He Tat-Seng Chua FAtt CML 96 14 0 21 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 178 423 0 20 Jan 2022
Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning Wenjie Shi Gao Huang Shiji Song Cheng Wu 81 11 0 06 Dec 2021
Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations Aida Mostafazadeh Davani Mark Díaz Vinodkumar Prabhakaran 91 315 0 12 Oct 2021
Shapley variable importance clouds for interpretable machine learning Yilin Ning M. Ong Bibhas Chakraborty B. Goldstein Daniel Ting Roger Vaughan Nan Liu FAtt 69 74 0 06 Oct 2021
Causal Explanations of Structural Causal Models Matej Zevcević Devendra Singh Dhami Constantin Rothkopf Kristian Kersting LRM 57 3 0 05 Oct 2021
Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation Thien Q. Tran Kazuto Fukuchi Youhei Akimoto Jun Sakuma CML 89 10 0 09 Sep 2021
Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanation Jiahui Li Kun Kuang Lin Li Long Chen Songyang Zhang Jian Shao Jun Xiao FAtt 123 19 0 03 Sep 2021
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey Richard Dazeley Peter Vamplew Francisco Cruz 72 65 0 20 Aug 2021
Neural-to-Tree Policy Distillation with Policy Improvement Criterion Zhaorong Li Yang Yu Yingfeng Chen Ke Chen Zhipeng Hu Changjie Fan 38 5 0 16 Aug 2021
FastSHAP: Real-Time Shapley Value Estimation N. Jethani Mukund Sudarshan Ian Covert Su-In Lee Rajesh Ranganath TDI FAtt 151 134 0 15 Jul 2021
Locally Sparse Neural Networks for Tabular Biomedical Data Junchen Yang Ofir Lindenbaum Y. Kluger 87 34 0 11 Jun 2021
Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning Tong Wang Jingyi Yang Yunyi Li Boxiang Wang FAtt 60 5 0 06 May 2021
Causality-based Counterfactual Explanation for Classification Models Tri Dung Duong Qian Li Guandong Xu CML 43 1 0 03 May 2021
Explaining a Series of Models by Propagating Shapley Values Hugh Chen Scott M. Lundberg Su-In Lee TDI FAtt 98 139 0 30 Apr 2021
Instance-wise Causal Feature Selection for Model Interpretation Pranoy Panda Sai Srinivas Kancheti V. Balasubramanian CML 110 16 0 26 Apr 2021
Generative Causal Explanations for Graph Neural Networks Wanyu Lin Hao Lan Baochun Li CML 67 179 0 14 Apr 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 166 72 0 02 Mar 2021
Benchmarking and Survey of Explanation Methods for Black Box Models F. Bodria F. Giannotti Riccardo Guidotti Francesca Naretto D. Pedreschi S. Rinzivillo XAI 127 234 0 25 Feb 2021
Feature Importance Explanations for Temporal Black-Box Models Akshay Sood M. Craven FAtt OOD 123 16 0 23 Feb 2021
Generative Interventions for Causal Learning Chengzhi Mao Augustine Cha Amogh Gupta Hongya Wang Junfeng Yang Carl Vondrick CML OOD 86 66 0 22 Dec 2020
Influence-Driven Explanations for Bayesian Network Classifiers Antonio Rago Emanuele Albini P. Baroni Francesca Toni 94 9 0 10 Dec 2020
Self-Explaining Structures Improve NLP Models Zijun Sun Chun Fan Qinghong Han Xiaofei Sun Yuxian Meng Leilei Gan Jiwei Li MILM XAI LRM FAtt 117 38 0 03 Dec 2020
Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation August DuMont Schütte J. Hetzel S. Gatidis Tobias Hepp Benedikt Dietz Stefan Bauer Patrick Schwab MedIm 103 70 0 29 Nov 2020
Explaining by Removing: A Unified Framework for Model Explanation Ian Covert Scott M. Lundberg Su-In Lee FAtt 144 252 0 21 Nov 2020
CDT: Cascading Decision Trees for Explainable Reinforcement Learning Zihan Ding Pablo Hernandez-Leal G. Ding Changjian Li Ruitong Huang 65 21 0 15 Nov 2020
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges Moloud Abdar Farhad Pourpanah Sadiq Hussain Dana Rezazadegan Li Liu ... Xiaochun Cao Abbas Khosravi U. Acharya V. Makarenkov S. Nahavandi BDL UQCV 371 1,951 0 12 Nov 2020
Feature Removal Is a Unifying Principle for Model Explanation Methods Ian Covert Scott M. Lundberg Su-In Lee FAtt 138 33 0 06 Nov 2020
Difference-in-Differences: Bridging Normalization and Disentanglement in PG-GAN Xiao-Yang Liu Jiajin Zhang Siting Li Zuotong Wu Yang Yu 37 0 0 16 Oct 2020
Pair the Dots: Jointly Examining Training History and Test Stimuli for Model Interpretability Yuxian Meng Chun Fan Zijun Sun Eduard H. Hovy Leilei Gan Jiwei Li FAtt 78 10 0 14 Oct 2020
Visualizing Color-wise Saliency of Black-Box Image Classification Models Yuhki Hatakeyama Hiroki Sakuma Yoshinori Konishi Kohei Suenaga FAtt 64 3 0 06 Oct 2020
Accurate and Robust Feature Importance Estimation under Distribution Shifts Jayaraman J. Thiagarajan V. Narayanaswamy Rushil Anirudh P. Bremer A. Spanias OOD 63 9 0 30 Sep 2020
CASTLE: Regularization via Auxiliary Causal Graph Discovery Trent Kyono Yao Zhang M. Schaar OOD CML 81 69 0 28 Sep 2020
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability Dylan Slack Sophie Hilgard Sameer Singh Himabindu Lakkaraju FAtt 140 163 0 11 Aug 2020
Causality Learning: A New Perspective for Interpretable Machine Learning Guandong Xu Tri Dung Duong Q. Li S. Liu Xianzhi Wang XAI OOD CML 62 52 0 27 Jun 2020
Generative causal explanations of black-box classifiers Matthew R. O’Shaughnessy Gregory H. Canal Marissa Connor Mark A. Davenport Christopher Rozell CML 96 73 0 24 Jun 2020
Why Attentions May Not Be Interpretable? Bing Bai Jian Liang Guanhua Zhang Hao Li Kun Bai Fei Wang FAtt 89 61 0 10 Jun 2020