arXiv:2508.14082v3 Announce Type: replace Abstract: Semi-supervised regression (SSR), which aims to predict continuous scores for samples while reducing the reliance on large-scale labeled data, has recently attracted considerable attention across various applications, including computer vision, natural language processing, audio analysis, and medical analysis. Existing SSR methods typically train models with scarce labeled data by introducing constraint-based regularization or ordinal ranking to mitigate overfitting. However, these approaches often fail to fully exploit the abundance of unlab

Source: arXiv cs.LG — read the full report at the original publisher.

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