The use of deep neural networks to make high risk decisions creates a need for global and local explanations so that users and experts have confidence in the modeling algorithms. We introduce a novel technique to find global and local explanations for time series data used in binary classification machine learning systems. We identify the most salient of the original features used by a black box model to distinguish between classes. The explanation can be made on categorical, continuous, and time series data and can be generalized to any binary classification model. The analysis is conducted on time series data to train a long short-term memory deep neural network and uses the time dependent structure of the underlying features in the explanation. The proposed technique attributes weights to features to explain an observations risk of belonging to a class as a multiplicative factor of a base hazard rate. We use a variation of the Cox Proportional Hazards regression, a Generalized Additive Model, to explain the effect of variables upon the probability of an in-class response for a score output from the black box model. The covariates incorporate time dependence structure in the features so the explanation is inclusive of the underlying time series data structure.
Score-based Conditional Out-of-Distribution Augmentation for Graph Covariate Shift
Institute for Clarity in Documentation（文档清晰研究所）；Inria Paris-Rocquencourt（巴黎-罗克琴堡研究所）；Rajiv Gandhi University（拉贾·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒姆研究实验室）；The Pennsylvania State University（宾夕法尼亚州立大学）
Conformalized Quantile Regression and Minimax Limits of Fixed-Score Calibration under Known Covariate Shift
HSE University（高等经济大学）；Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）；Laboratoire de Recherche d’EPITA（EPITA研究实验室）；University of Duisburg-Essen（杜伊斯堡-埃森大学）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学（MBZUAI））
GDDA: Semantic OOD Detection on Graphs under Covariate Shift via Score-Based Diffusion Models
School of New Media and Communication, Tianjin University（天津大学新媒体与传播学院）；Baylor University（贝勒大学）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）
Propensity score estimation using classification and regression trees in the presence of missing covariate data
Leiden University Medical Center（莱顿大学医学中心）
数据挖掘和机器学习技术，如分类和回归树（CART）代表了一个有前途的替代传统的逻辑斯谛回归的倾向得分估计。尽管不完整的数据排除了对所有受试者进行逻辑斯谛回归的拟合，但CART之所以吸引人，部分原因是某些实现允许将不完整的记录并入树拟合中，并为所有受试者提供倾向得分估计。基于理论上的考虑，我们认为，自动处理缺失数据的CART可能是不合适的。使用一系列模拟实验，我们检查了不同方法处理缺失协变量数据的性能;（i）将CART算法直接应用于（部分）不完整的数据，（ii）完整的案例分析，（iii）多重插补。评估了评估确认-结局效应\add{among the exposed}的偏倚、标准误、均方误差和覆盖率。将CART算法直接应用于不完整的数据会导致偏倚，即使在数据完全随机完全随机缺失的情况下也是如此。总体而言，多重插补后加CART的结果最佳。我们的研究表明，在CART中自动处理缺失数据可能会导致严重偏倚，并且作为解释缺失数据的一种手段，其性能并不优于多重插补。
Learning covariate importance for matching in policy-relevant observational research
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA（生物统计学、流行病学与信息学系，宾夕法尼亚大学佩雷尔曼医学学院，费城，PA）；School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), China（数据科学学院，香港中文大学（深圳）（CUHK-Shenzhen），中国）
