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 …

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 …

Given a single trajectory of a dynamical system, we analyze the performance of the
nonparametric least squares estimator (LSE). More precisely, we give nonasymptotic …

A major challenge in designing efficient statistical supervised learning algorithms is finding
representations that perform well not only on available training samples but also on unseen …

We present a new framework for deriving bounds on the generalization bound of statistical
learning algorithms from the perspective of online learning. Specifically, we construct an …

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 present a variety of novel information-theoretic generalization bounds for
learning algorithms, from the supersample setting of Steinke & Zakynthinou (2020)-the …