arXiv:2607. 08579v1 Announce Type: cross Abstract: Missing data is a persistent obstacle in scientific, social science, and public health research, often biasing analyses and placing accountability on analysts for how they handle missing values.
By Aitik Dandapat, Lalith Punepalle Raveendrareddy, Mithilesh Kumar Singh, Klaus Mueller
arXiv:2607. 29177v1 Announce Type: cross Abstract: Utility data (e.
By Rongchao Xu, Lin Jiang, Dahai Yu, Ximiao Li, Guang Wang
arXiv:2510. 02625v5 Announce Type: replace Abstract: Missing data in tabular datasets forces practitioners into a hard choice: deploy a general-purpose imputer that may perform poorly for the problem at hand, or wait for someone to design a specialized algorithm.
By Jacob Feitelberg, Dwaipayan Saha, Kyuseong Choi, Zaid Ahmad, Anish Agarwal, Raaz Dwivedi
arXiv:2606. 17106v1 Announce Type: new Abstract: Laboratory tests in electronic health records are collected irregularly, and the absence of a test order can be as informative as the measurement itself.
By Hadi Mehdizavareh, Gabriele Santangelo, Giovanna Nicora, Simon Lebech Cichosz, Arianna Dagliati, Arijit Khan, Riccardo Bellazzi
arXiv:2606. 06328v1 Announce Type: new Abstract: In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because electrodes detach or an entire respiratory channel is unavailable during overnight monitoring.
By Ziwen Kan, Wugeng Zheng, Tianlong Chen, Song Wang
arXiv:2606. 05073v1 Announce Type: new Abstract: Missing value imputation is a fundamental task in machine learning, with most existing methods assuming that all missing entries correspond to unobserved regular values.
By Lixing Zhang, Yidong Ouyang, Weifu Li, Shixiang Zhu, Guang Cheng, Liyan Xie