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Preprint Sep 2026

Design-Assisted Regression

We consider regression problems in which the marginal distribution of the covariates is informative for estimation and variable selection, rather than merely auxiliary. Motivated by random-design, high-dimensional, and latent-effect settings, we propose a general design-assisted regression framework in which the estima...

S. Ye, Guan-Bo Wang, Cong Zhang et al. · 0 citations
#machine learning Preprint Sep 2026

RiVaT-Fuse: Reliability-Calibrated Variational Tensor Fusion for Multimodal Prediction under Modality Uncertainty

Image-metadata prediction requires fusing heterogeneous evidence whose reliability can vary across samples and latent factors. Existing representation-level fusion methods typically choose an aggregation architecture, such as concatenation, gating, conditional modulation, or attention, without explicitly defining what...

Yin Xu, Tie-Ming Liu, Ye Liang et al. · 0 citations

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