On the Asymptotics of Self-Supervised Pre-training: Two-Stage M-Estimation and Representation Symmetry
Self-supervised pre-training, where large corpora of unlabeled data are used to learn representations for downstream fine-tuning, has become a cornerstone of modern machine learning. While a growing body of theoretical work has begun to analyze this paradigm, existing bounds leave open the question of how sharp the current rates are, and whether they accurately capture the complex interaction between pre-training and fine-tuning. In this paper, we address this gap by developing an asymptotic theory of pre-training via two-stage M-estimation. A key challenge is that the pre-training estimator is often identifiable only up to a group symmetry, a feature common in representation learning tha...
| Authors | Mohammad Tinati, Stephen Tu |
|---|---|
| Primary category | cs.LG |
| Published | 29 March 2026 |
| Last updated at source | 1 July 2026 |
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