arXiv:2504. 15388v3 Announce Type: replace-cross Abstract: In the context of multivariate nonparametric regression with missing covariates, we propose Pattern Embedded Neural Networks (PENNs), which can be applied in conjunction with any existing imputation technique.
By Tianyi Ma, Tengyao Wang, Richard J. Samworth
arXiv:2609.39613v1 Announce Type: new
Abstract: Missing data are a fundamental challenge in statistical analysis and machine learning, as the choice of imputation method substantially impacts downstr...
By Jinwei Li, Michelle Bruch, Daniel Tenbrinck
arXiv:2609.37632v1 Announce Type: cross
Abstract: Time series imputation has progressed from statistical and deep learning approaches to diffusion-based models, which have shown strong recent perform...
By Fariza Rashid, Duc Van Le, Rahat Masood, Gustavo Batista, Aruna Seneviratne, Suranga Seneviratne
The paper introduces the Masked Diffusion Time-series Imputation Model (MDTIM), which uses a masked diffusion training paradigm to directly predict original values for time series imputation. It separates missing and observed data via a MASK token and employs Stochastic Discretization to convert continuous values into ordinal-aware tokens, preserving temporal dynamics. Experiments on multiple benchmarks show that MDTIM outperforms existing deterministic and generative baselines in robustness and scalability across various missing data scenarios.
By Dongbin Kim, Seungyun Lee, Geonwoo Shin, Jaewook Lee
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.
Probabilistic Regression Trees (PRTrees) are a smooth and consistent alternative to classical regression trees, producing continuous predictions through probabilistic split assignments. This paper extends the PRTree framework to accommodate missing predictor values directly during tree construction, eliminating the need for prior imputation.