arXiv:2607. 03641v1 Announce Type: cross Abstract: The manifold hypothesis posits that high-dimensional data are concentrated near a low-dimensional embedded manifold.
arXiv:2602. 07875v3 Announce Type: replace Abstract: Generating tabular data under conditions is critical to applications requiring precise control over the generative process.
arXiv:2608.30040v1 Announce Type: cross Abstract: Missing data, measurement error, and population heterogeneity are pervasive challenges in analyzing data arising from modern observational studies an...
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...
The paper introduces the Robust Graph Clustering Network for Multiple Missing Data (RGCN), a method designed to cluster graphs with simultaneous missing node attributes and structural links. RGCN employs a view‑decoupled dual‑branch imputation to reduce cross‑view interference, a multi‑hyperspherical mixture prior to improve cluster compactness and separability on a directional latent manifold, and a boundary‑aware contrastive enhancement objective to counteract cluster blurring caused by imputation bias. Experiments on real‑world datasets show that RGCN consistently outperforms state‑of‑the‑art baselines across various missing data patterns.
arXiv:2608. 04827v1 Announce Type: cross Abstract: We introduce the Intrinsic Hybrid Latent Diffusion Model (ILDM), a generative framework that integrates probabilistic dimensionality reduction with geometry-aware diffusion on unknown manifolds.
We introduce the Intrinsic Hybrid Latent Diffusion Model (ILDM), a generative framework that integrates probabilistic dimensionality reduction with geometry-aware diffusion on unknown manifolds. While diffusion models (DMs) have achieved state-of-the-art results in high-dimensional data synthesis, they rely on large training datasets and ignore intrinsic geometric structure.
arXiv:2607. 17390v1 Announce Type: cross Abstract: Kernel regression with tensor trains and Hadamard overparameterization (KReTTaH) is introduced as a training-data-free, interpretable, and nonparametric framework for multi-way data imputation.
arXiv:2607. 06930v1 Announce Type: cross Abstract: Missing data is prevalent in practical applications, making effective imputation an essential preprocessing step for downstream analysis.
arXiv:2607. 23295v1 Announce Type: cross Abstract: In real-world machine learning applications, incomplete observations create a fundamental challenge.
arXiv:2609.15284v1 Announce Type: new Abstract: Missing values are ubiquitous in heterogeneous data mining, where numerical, categorical, and binary variables often coexist. Many imputation methods,...
arXiv:2609.00616v1 Announce Type: cross Abstract: Matrix-variate data with missing entries arise frequently in applications where observations are naturally organized as two-dimensional arrays. Altho...
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.