Deep neural networks excel in supervised learning tasks but are constrained by the need for
extensive labeled data. Self-supervised learning emerges as a promising alternative …

We propose a scheme for auditing differentially private machine learning systems with a
single training run. This exploits the parallelism of being able to add or remove multiple …

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 …

Deep learning is usually described as an experiment-driven field under continuous criticizes
of lacking theoretical foundations. This problem has been partially fixed by a large volume 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 …

Abstract Spiking Neural Network (SNN), recognized as a type of biologically plausible
architecture, has recently drawn much research attention. It transmits information by $0/1 …

Numerous deep learning algorithms have been inspired by and understood via the notion of
information bottleneck, where unnecessary information is (often implicitly) minimized while …

We study the generalization properties of the popular stochastic optimization method known
as stochastic gradient descent (SGD) for optimizing general non-convex loss functions. Our …

The information-theoretic framework of Russo and Zou (2016) and Xu and Raginsky (2017)
provides bounds on the generalization error of a learning algorithm in terms of the mutual …

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 …