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News/The Loss Is Not Enough: Sampling Conditions and Inductive Bias in Contrastive Representation Learning
arxiv
PublishedJune 5, 2026 at 4:00 AM
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The Loss Is Not Enough: Sampling Conditions and Inductive Bias in Contrastive Representation Learning

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arXiv:2606.04280v1 Announce Type: cross Abstract: Contrastive learning has become a leading paradigm for self-supervised representation learning, yet the conditions under which it recovers meaningful latent geometry remain incompletely understood. We develop a measure-theoretic framework formalizing

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