Awesome XAI

[sijic]

Survey, Tutorial, Book

• Explainable AI (XAI): Core Ideas, Techniques and Solutions, ACM Computing Survey 2022
• A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods, FaccT 2022
• Interpretable machine learning:Fundamental principles and 10 grand challenges, Statist. Survey 2022
• [2103.11251] Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
• [2101.09429] Explainable Artificial Intelligence Approaches: A Survey
• [2107.07045] Explainable AI: current status and future directions
• Explainable artificial intelligence: A survey | IEEE Conference Publication | IEEE Xplore
• Explainable artificial intelligence : an analytical review
• A historical perspective of explainable Artificial Intelligence
• A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence
• Entropy | Free Full-Text | Explainable AI: A Review of Machine Learning Interpretability Methods
• A Survey on Neural Network Interpretability | IEEE Journals & Magazine | IEEE Xplore
• Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications | IEEE Journals & Magazine | IEEE Xplore
  – mention first-order
• The Principles of Deep Learning Theory by FAIR
• MODEL INTERPRETATION AND DATA VALUATION FOR MACHINE LEARNING
• [2103.11251] Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
• Cheatsheet
• Four principles of explainable artificial intelligence
• Book Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
• TAMU Interpretable Machine Learning: Concepts and Techniques
• Explainable Machine Learning
• AAAI 21 Explaining Machine Learning Predictions: State-of-the-Art, Challenges, and Opportunities
• AAAI 21 From Explainability to Model Quality and Back Again
• NIPS 20 Explaining Machine Learning Predictions: State-of-the-art, Challenges, Opportunities (GitHub Link)
• IJCAI 20 Trustworthiness of Interpretable Machine Learning
• CVPR 20 Interpretable Machine Learning for Computer Vision
• GCPR 17 Tutorial on Interpretable Machine Learning
• MICCAI 18 Tutorial on Interpretable Machine Learning
• ICML 17 Interpretable Machine Learning:The fuss, the concrete and the questions [Video]
• A Survey of Methods for Explaining Black Box Models
• Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
• Explainability in deep reinforcement learning - ScienceDirect

Repo

GitHub - wangyongjie-ntu/Awesome-explainable-AI: A collection of research...

[Rui]

Graph Interpretable

• GNNExplainer: Generating Explanations for Graph Neural Networks (neurips.cc) 2019 420
• GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks | IEEE Journals & Magazine | IEEE Xplore 2022 171
• BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis - ScienceDirect 2021 20
• Phys. Rev. Lett. 120, 145301 (2018) - Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties (aps.org) 2018 517
• [2107.05097] BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis (arxiv.org)

From 2022

• GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks | IEEE Journals & Magazine | IEEE Xplore 171
• Interpretable machine learning for knowledge generation in heterogeneous catalysis | Nature Catalysis 11
• Graph Neural Networks | SpringerLink
• Interpretable machine learning: Fundamental principles and 10 grand challenges (projecteuclid.org)

2021

• Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising | IEEE Journals & Magazine | IEEE Xplore
• On Interpretability of Artificial Neural Networks: A Survey | IEEE Journals & Magazine | IEEE Xplore (Survey)
• Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond (arxiv.org)]([2103.10689] Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond)
• Generative Causal Explanations for Graph Neural Networks (mlr.press)
• Towards Self-Explainable Graph Neural Network | Proceedings of the 30th ACM International Conference on Information & Knowledge Management
• Interpretable and Efficient Heterogeneous Graph Convolutional Network | IEEE Journals & Magazine | IEEE Xplore
• Graph Neural Networks with Heterophily | Proceedings of the AAAI Conference on Artificial Intelligence
• Explainability-based Backdoor Attacks Against Graph Neural Networks | Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning
• An Overview on the Application of Graph Neural Networks in Wireless Networks | IEEE Journals & Magazine | IEEE Xplore

2020

• A Comprehensive Survey on Graph Neural Networks | IEEE Journals & Magazine | IEEE Xplore (survey)
• CVPR 2020 Open Access Repository (thecvf.com)
• XGNN: Towards Model-Level Explanations of Graph Neural Networks | Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
• [2012.15445] Explainability in Graph Neural Networks: A Taxonomic Survey (arxiv.org)
• Evaluating Attribution for Graph Neural Networks (neurips.cc)

[sijic]

2021
Frontiers in Big Data Principles and Practice of Explainable Machine Learning

What Do We Want From Explainable Artificial Intelligence (XAI)? – A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research

Artificial intelligence explainability: the technical and ethical dimensions

Explainable artificial intelligence: a comprehensive review

Classification of Explainable Artificial Intelligence Methods through Their Output Formats