arXiv AI
Jul 28
FILLER: Feature Imputation via Latent Location Exploration and Retrieval
arXiv:2607. 23295v1 Announce Type: cross Abstract: In real-world machine learning applications, incomplete observations create a fundamental challenge.
Flow Matching with Missing Data
arXiv:2607. 28698v1 Announce Type: new Abstract: Flow matching assumes fully observed training data, which many real-world applications rarely provide.
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arXiv Machine Learning
Jun 4
Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness
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.
arXiv AI
Jun 3
AugMask: Training Diffusion Models on Incomplete Tabular Data via Stochastic Augmentation and Masking
arXiv:2606. 03347v1 Announce Type: cross Abstract: Score-based diffusion models have emerged as prominent deep generative models; however, their application to tabular data remains challenging because their backbones assume fully specified inputs, whereas real-world tabular data often contain missing values.
Hugging Face Trending Papers
Jun 3
Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness
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 Machine Learning
Jun 8
Spatiotemporal Imputation with Graph-Informed Flow Matching
arXiv:2606. 06682v1 Announce Type: new Abstract: Missing data is a common challenge in spatiotemporal systems, arising in applications such as air quality monitoring and urban traffic management.
arXiv Machine Learning
Jul 10
Distributionally Faithful Imputation via Positive Semi-Definite Kernel Density Estimation
arXiv:2607. 07767v1 Announce Type: cross Abstract: Missing values undermine statistical inference and machine learning pipelines, yet most imputation methods rely on heuristics or restrictive parametric assumptions that ignore the joint data distribution.
arXiv Machine Learning
Jun 4
Rethinking Incompleteness: Formalizing Protocol Divergence and Train-Once Learning for Robust IMVC
arXiv:2606. 04857v1 Announce Type: new Abstract: Standard IMVC evaluation retrains separate models for different missing-data configurations.
arXiv Machine Learning
Jun 5
PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data
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.
arXiv Machine Learning
Jun 2
Exploring and Exploiting Stability in Latent Flow Matching
arXiv:2605. 08398v2 Announce Type: replace Abstract: In this work, we show that Latent Flow-Matching (LFM) models are robust to different types of perturbations, including data reduction and model capacity shrinkage.
arXiv Machine Learning
Jul 2
Deep learning with missing data
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.
arXiv Machine Learning
Jul 17
Learning Who to Treat When Treatment is Missing
arXiv:2607. 14346v1 Announce Type: new Abstract: Policy learning methods are increasingly used to inform treatment allocation under budget constraints.
arXiv AI
Jul 9
Imputation Meets Clustering: Exploiting Latent Subgroup Structure for Missing Data Recovery
arXiv:2607. 06930v1 Announce Type: cross Abstract: Missing data is prevalent in practical applications, making effective imputation an essential preprocessing step for downstream analysis.