Even Sharper Bounds for Transductive Learning and Its Applications
We introduce Sharper Transductive Local Complexity (STLC), a localized complexity method for transductive learning under uniform sampling without replacement. The construction starts from a Bernstein-type concentration inequality for the supremum of the test--train empirical process. Its proof uses the modified log-Sobolev inequality for the swap walk and a two-parameter entropy closure. A peeling argument with a surrogate localization functional then gives excess-risk bounds with the same fixed-point and confidence terms as the classical inductive local Rademacher-complexity bounds, without the additional logarithmic confidence factor in earlier transductive results. For realizable learn...
| Authors | Yingzhen Yang |
|---|---|
| Primary category | cs.LG |
| Published | 23 September 2026 |
| Last updated at source | 24 September 2026 |
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