Various approaches have been developed to upper bound the generalization error of a
supervised learning algorithm. However, existing bounds are often loose and lack of …

A fundamental question in theoretical machine learning is generalization. Over the past
decades, the PAC-Bayesian approach has been established as a flexible framework to …

To date, no “information-theoretic” frameworks for reasoning about generalization error have
been shown to establish minimax rates for gradient descent in the setting of stochastic …

Understanding generalization in modern machine learning settings has been one of the
major challenges in statistical learning theory. In this context, recent years have witnessed …

The emergence of powerful deep learning architectures has resulted in breakthrough
innovations in several fields such as healthcare, precision farming, banking, education, and …

We provide an information-theoretic analysis of the generalization ability of Gibbs-based
transfer learning algorithms by focusing on two popular empirical risk minimization (ERM) …

Generalization error bounds are essential to understanding machine learning algorithms.
This paper presents novel expected generalization error upper bounds based on the …

Various approaches have been developed to upper bound the generalization error of a
supervised learning algorithm. However, existing bounds are often loose and even vacuous …

In this paper, we establish novel data-dependent upper bounds on the generalization error
through the lens of a “variable-size compressibility” framework that we introduce newly here …

The ability of machine learning (ML) algorithms to generalize well to unseen data has been
studied through the lens of information theory, by bounding the generalization error with the …