Aggregated predictors are obtained by making a set of basic predictors vote according to
some weights, that is, to some probability distribution. Randomized predictors are obtained …

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 …

Deep learning has achieved remarkable success in many machine learning tasks such as
image classification, speech recognition, and game playing. However, these breakthroughs …

Recent work has established that the conditional mutual information (CMI) framework of
Steinke and Zakynthinou (2020) is expressive enough to capture generalization guarantees …

Meta learning aims at learning a model that can quickly adapt to unseen tasks. Widely used
meta learning methods include model agnostic meta learning (MAML), implicit MAML …

This paper uses information-theoretic tools to analyze the generalization error in
unsupervised domain adaptation (UDA). We present novel upper bounds for two notions of …

Bernstein's condition is a key assumption that guarantees fast rates in machine learning. For
example, the Gibbs algorithm with prior $\pi $ has an excess risk in $ O (d_ {\pi}/n) $, as …

Meta learning has attracted much attention recently in machine learning community.
Contrary to conventional machine learning aiming to learn inherent prediction rules to …

PAC-Bayes has recently re-emerged as an effective theory with which one can derive
principled learning algorithms with tight performance guarantees. However, applications of …

Meta-learning aims at optimizing the hyperparameters of a model class or training algorithm
from the observation of data from a number of related tasks. Following the setting of Baxter …