Natural policy gradient (NPG) methods, equipped with function approximation and entropy
regularization, achieve impressive empirical success in reinforcement learning problems …

Learning tasks play an increasingly prominent role in quantum information and computation.
They range from fundamental problems such as state discrimination and metrology over the …

In this work, we investigate the interplay between memorization and learning in the context
of\emph {stochastic convex optimization}(SCO). We define memorization via the information …

We provide a new information-theoretic generalization error bound that is exactly tight (ie,
matching even the constant) for the canonical quadratic Gaussian (location) problem. Most …

We investigate the in-distribution generalization of machine learning algorithms. We depart
from traditional complexity-based approaches by analyzing information-theoretic bounds …

The empirical risk minimization (ERM) problem with relative entropy regularization (ERM-
RER) is investigated under the assumption that the reference measure is a σ-finite measure …

This paper contains a recipe for deriving new PAC-Bayes generalisation bounds based on
the $(f,\Gamma) $-divergence, and, in addition, presents PAC-Bayes generalisation bounds …

In this paper, we develop data-dependent and algorithm-dependent generalization bounds
for transductive learning algorithms in the context of information theory for the first time. We …

The safe integration of machine learning modules in decision-making processes hinges on
their ability to quantify uncertainty. A popular technique to achieve this goal is conformal …

The effect of the relative entropy asymmetry is analyzed in the empirical risk minimization
with relative entropy regularization (ERM-RER) problem. A novel regularization is …