Recently, increased computational power and data availability, as well as algorithmic
advances, have led machine learning (ML) techniques to impressive results in regression …

Learning in the presence of outliers is a fundamental problem in statistics. Until recently, all
known efficient unsupervised learning algorithms were very sensitive to outliers in high …

One of the most important properties of classical neural networks is how surprisingly
trainable they are, though their training algorithms typically rely on optimizing complicated …

Quantum error mitigation has been proposed as a means to combat unwanted and
unavoidable errors in near-term quantum computing without the heavy resource overheads …

It is currently known how to characterize functions that neural networks can learn with SGD
for two extremal parametrizations: neural networks in the linear regime, and neural networks …

Why are classifiers in high dimension vulnerable to “adversarial” perturbations? We show
that it is likely not due to information theoretic limitations, but rather it could be due to …

In this survey, we review work in machine learning on methods for handling data sets
containing large amounts of irrelevant information. We focus on two key issues: the problem …

We describe a general technique that yields the first Statistical Query lower bounds for a
range of fundamental high-dimensional learning problems involving Gaussian distributions …

Learning problems form an important category of computational tasks that generalizes many
of the computations researchers apply to large real-life data sets. We ask, What concept …

Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani
introduce a number of central topics in computational learning theory for researchers and …