Improved excess risk bounds for extreme multi-class supervised contrastive representation learning achieve sample complexity of order R (number of classes) or O(k) (samples per tuple) independent of the rarest class probability.
2bRT iidr (H) + 10B s ln 4/δ 2⌊Nr/2⌋ # (∗) ⩽ BR N + X r:Nr⩾2 bρr
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning
Improved excess risk bounds for extreme multi-class supervised contrastive representation learning achieve sample complexity of order R (number of classes) or O(k) (samples per tuple) independent of the rarest class probability.