An information-theoretic upper bound on the generalization error of supervised learning
algorithms is derived. The bound is constructed in terms of the mutual information between …

In this work, we improve upon the stepwise analysis of noisy iterative learning algorithms
initiated by Pensia, Jog, and Loh (2018) and recently extended by Bu, Zou, and Veeravalli …

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 …

Bounding the generalization error of learning algorithms has a long history, which yet falls
short in explaining various generalization successes including those of deep learning. Two …

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 …

In this work, the probability of an event under some joint distribution is bounded by
measuring it with the product of the marginals instead (which is typically easier to analyze) …

Machine learning algorithms operating on mobile networks can be characterized into three
different categories. First is the classical situation in which the end-user devices send their …

We capitalize on the Wasserstein distance to obtain two information-theoretic bounds on the
generalization error of learning algorithms. First, we specialize the Wasserstein distance into …

Federated zeroth-order optimization (FedZO) algorithm enjoys the advantages of both zeroth-
order optimization and federated learning, and has shown exceptional performance on …