# Asymptotic sequential Rademacher complexity of a finite function class

arXiv: https://arxiv.org/abs/1605.03843 · HTML: https://mlsanity.com/paper/1605.03843/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2016.

## Built on by (most cited first, 1 of 1)

- [Gradual Fine-Tuning with Graph Routing for Multi-Source Unsupervised Domain Adaptation](https://mlsanity.com/paper/2411.07185/) (2024)

## Abstract (arXiv)

For a finite function class we describe the large sample limit of the sequential Rademacher complexity in terms of the viscosity solution of a G-heat equation. In the language of Peng's sublinear expectation theory, the same quantity equals to the expected value of the largest order statistics of a multidimensional G-normal random variable. We illustrate this result by deriving upper and lower bounds for the asymptotic sequential Rademacher complexity.