AI RESEARCH
A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning
arXiv CS.LG
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ArXi:2605.07596v1 Announce Type: cross Contrastive Representation Learning (CRL) has achieved strong empirical success in multiple machine learning disciplines, yet its theoretical sample complexity remains poorly understood. Existing analyses usually assume that input tuples are identically and independently distributed, an assumption violated in most practical settings where contrastive tuples are constructed from a finite pool of labeled data, inducing dependencies among tuples.