Missing value imputation is a fundamental task in machine learning, with most existing methods assuming that all missing entries correspond to unobserved regular values. In many real-world datasets, however, missingness may arise from two distinct sources: some entries are meaningfully missing (intrinsically absent and semantically valid), while others are missing due to the observation process and should be imputed.
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
arXiv:2602. 07875v3 Announce Type: replace Abstract: Generating tabular data under conditions is critical to applications requiring precise control over the generative process.
By Aditya Shankar, Yuandou Wang, Rihan Hai, Lydia Y. Chen
arXiv:2607. 28698v1 Announce Type: new Abstract: Flow matching assumes fully observed training data, which many real-world applications rarely provide.
By Fairoz Nower Khan, Nabuat Zaman Nahim, Peizhong Ju
Recent advances in diffusion models have shown impressive performance in controllable image generation and dense prediction tasks. However, existing approaches typically treat diffusion-based controllable generation and dense prediction as separate tasks, overlooking the potential benefits of jointly modeling the heterogeneous distributions.
arXiv:2512. 20963v3 Announce Type: replace Abstract: Diffusion models excel at generating high-quality, diverse samples, yet they risk memorizing training data when overfit to the training objective.
By Zekai Zhang, Xiao Li, Xiang Li, Lianghe Shi, Meng Wu, Molei Tao, Qing Qu