Can I read Sharp Generalization of Transductive Learning: A Transductive Local Rademacher Complexity Approach on EtoBox?

Sharp Generalization of Transductive Learning: A Transductive Local Rademacher Complexity Approach by Yang, Yingzhen is a scholarly article available to read on EtoBox.

What is Sharp Generalization of Transductive Learning: A Transductive Local Rademacher Complexity Approach about?

We introduce a new tool, Transductive Local Complexity (TLC), designed to analyze the generalization performance of transductive learning methods and inspire the development of new algorithms in this domain. Our work extends the concept of the popular Local Rademacher Complexity (LRC) to the transductive setting, incorporating significant and novel modifications compared to the typical analysis of LRC methods in the inductive setting. While LRC has been widely used as a powerful tool for analyzing inductive models, providing sharp generalization bounds for classification and minimax rates for nonparametric regression, it remains an open question whether a localized Rademacher complexity-based tool can be developed for transductive learning. Our goal is to achieve sharp bounds for transductive learning that align with the inductive excess risk bounds established by LRC. We provide a definitive answer to this open problem with the introduction of TLC. We construct TLC by first establishing a novel and sharp concentration inequality for the supremum of a test-train empirical processes. Using a peeling strategy and a new surrogate variance operator, we derive the a novel excess risk bo

Author

Yang, Yingzhen

Published

2023

Language

EN