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 …

The increasing digitization and datification of all aspects of people's daily life, and the
consequent growth in the use of personal data, are increasingly challenging the current …

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 …

Since the celebrated works of Russo and Zou (2016, 2019) and Xu and Raginsky (2017), it
has been well known that the generalization error of supervised learning algorithms can be …

We present a new family of information-theoretic generalization bounds, in which the
training loss and the population loss are compared through a jointly convex function. This …

We present new information-theoretic generalization guarantees through the a novel
construction of the" neighboring-hypothesis" matrix and a new family of stability notions …

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 …

This century saw an unprecedented increase of public and private investments in Artificial
Intelligence (AI) and especially in (Deep) Machine Learning (ML). This led to breakthroughs …

This paper presents a general methodology for deriving information-theoretic generalization
bounds for learning algorithms. The main technical tool is a probabilistic decorrelation …

This paper explains the generalization power of a deep neural network (DNN) from the
perspective of interactions. Although there is no universally-accepted definition of the …