Statistical Consistency and Generalization of Contrastive Representation Learning

arXiv:2605.02116v2 Announce Type: replace Abstract: Contrastive representation learning (CRL) underpins many modern foundation models. Despite recent theoretical progress, existing analyses suffer from several key limitations: (i) the statistical consistency of CRL remains poorly understood; (ii) available generalization bounds deteriorate as the number of negative samples increases, contradicting the empirical benefits of large negative sets; and (iii) the retrieval performance of CRL has received limited theoretical attention. In this paper, we develop a unified statistical learning theory f

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

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