Our goal is to bound the generalization error in such a way that the training error converges to the generalization error. But we are proving it ...

which means we can bound the generalization error of a hypothesis in terms of its empirical error and the Rademacher complexity of the class of loss functions.

Rademacher complexity, named after Hans Rademacher, measures richness of a class of sets with respect to a probability distribution.

8 авг. 2022 г. · We show that the Rademacher complexity-based approach can generate non-vacuous generalisation bounds on Convolutional Neural Networks (CNNs)

A Rademacher complexity and generalization bounds. Herein we briefly review Rademacher complexity, a widely used concept in deriving generalization bounds ...

The Rademacher complexity bound has no explicit dependency on the depth of the network, while the generalization bounds are comparable to the Monte Carlo error.

30 янв. 2017 г. · In this lecture, we discuss Rademacher complexity, which is a differ- ent (and often better) way to obtain generalization bounds for learning.

4 июл. 2023 г. · We propose a conceptually related, but technically distinct complexity measure to control generalization error, which is the empirical Rademacher complexity.

This paper presents the first data-dependent generalization bounds for non-iid settings based on the notion of Rademacher complexity.

We prove margin bounds using the surrogate loss and show that if the weight matrix of the first layer has bounded `1 norm, the margin bound does not have.