Towards Ground Truth Explainability on Tabular Data
Center for Machine Learning, Capital One（第一资本机器学习中心）；Tandon School of Engineering, New York University（纽约大学坦登工程学院）；Center for Data Science, New York University（纽约大学数据科学中心）；Card Machine Learning and Technology, Capital One（第一资本卡片机器学习与技术部）
Brian Barr, Ke Xu, Claudio Silva, Enrico Bertini, Robert Reilly, C. Bayan Bruss, Jason D. Wittenbach
In data science, there is a long history of using synthetic data for method development, feature selection and feature engineering. Our current interest in synthetic data comes from recent work in explainability. Today's datasets are typically larger and more complex - requiring less interpretable models. In the setting of \textit{post hoc} explainability, there is no ground truth for explanations. Inspired by recent work in explaining image classifiers that does provide ground truth, we propose a similar solution for tabular data. Using copulas, a concise specification of the desired statistical properties of a dataset, users can build intuition around explainability using controlled data sets and experimentation. The current capabilities are demonstrated on three use cases: one dimensional logistic regression, impact of correlation from informative features, impact of correlation from redundant variables.
Evaluating Black-Box Vulnerabilities with Wasserstein-Constrained Data Perturbations
INRIA Regalia & ANITI Institut de Mathématiques de Toulouse, France Centre INRIA de l’Université de Bordeaux（INRIA里加大学与ANITI数学研究所，法国图卢兹数学研究所，INRIA波尔多中心）
Evaluating Explainability for Graph Neural Networks
Adobe（奥多比公司）；University of Tennessee（田纳西大学）；Harvard Business School（哈佛商学院）；Harvard University（哈佛大学）；Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学博德研究所）；Harvard Data Science Initiative（哈佛数据科学研究院）
TabStruct: Measuring Structural Fidelity of Tabular Data
Department of Computer Science and Technology, University of Cambridge（计算机科学与技术系，剑桥大学）；Télécom Paris, Institut Polytechnique de Paris（巴黎理工 Télécom Paris）
Explainability in mulimodal deep transformation models for stroke outcome prediction
University Hospital Zurich, Department of Neurology（苏黎世大学医院神经内科）；Zurich University of Applied Sciences, Institute of Data Analysis and Process Design (IDP)（苏黎世应用科学大学数据分析与流程设计研究所）；University of Zurich, Epidemiology, Biostatistics and Prevention Institute (EBPI)（苏黎世大学流行病学、生物统计学与预防研究所）；Thurgau Institute for Digital Transformation (TIDIT)（图尔高数字转型研究所）
TabSCM: A practical Framework for Generating Realistic Tabular Data
Federal Institute for Occupational Safety and Health (BAuA)（联邦职业安全与健康研究所）；School of Computation, Information and Technology, Technical University of Munich（计算、信息与技术学院，慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
ShaTS: A Shapley-based Explainability Method for Time Series Artificial Intelligence Models applied to Anomaly Detection in Industrial Internet of Things
Departamento de Ingeniería y Tecnología de Computadores, University of Murcia（计算机工程与技术部门，穆尔西亚大学）
A Unified Evaluation Framework for Multi-Annotator Tendency Learning
GSFS, The University of Tokyo（东京大学GSFS中心）；IST, The University of Osaka（大阪大学IST中心）；D3 Center, The University of Osaka（大阪大学D3中心）；SANKEN, The University of Osaka（大阪大学SANKEN中心）；SI, Xiamen University（厦门大学SI中心）；KAIUS, Tongji University（同济大学KAIUS中心）
CausalWrap: Model-Agnostic Causal Constraint Wrappers for Tabular Synthetic Data
Department of Biostatistics, Vanderbilt University Medical Center（生物统计学系，范德比尔特大学医学中心）；Department of Biomedical Informatics, Vanderbilt University Medical Center（生物医学信息学系，范德比尔特大学医学中心）
MoE-Enhanced Explainable Deep Manifold Transformation for Complex Data Embedding and Visualization
Centre for Artificial Intelligence and Robotics (CAIR), HKISI-CAS and Westlake University（人工智能与机器人中心（CAIR），HKISI-CAS和西湖大学）；Westlake University（西湖大学）；School of Information and Electrical Engineering, Hangzhou City University（信息与电气工程学院，杭州市大学）；Academy of Edge Intelligence Hangzhou City University（边缘智能学院，杭州市大学）；CAIR, HKISI-CAS（人工智能与机器人中心（CAIR），HKISI-CAS）；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), CASIA（多模态人工智能系统国家重点实验室（MAIS），CASIA）；School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)（人工智能学院，中国科学院大学（UCAS））；Ant Group（蚂蚁集团）
ExDBSCAN: Explaining DBSCAN with Counterfactual Reasoning -- Additional Material
Aarhus University Department of Computer Science（奥胡斯大学计算机科学系）；Forschungszentrum Jülich（朱利奇研究中心）；LMU Munich（慕尼黑大学）；University of Southern Denmark Department of Computer Science and Mathematics（南丹麦大学计算机科学与数学系）；MCML Munich Germany（慕尼黑MCML德国）；DBS
