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 …

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 …

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 …

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

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 …

In this paper, we study the problem of learning in the presence of classification noise in the
probabilistic learning model of Valiant and its variants. In order to identify the class of …

We describe a slightly subexponential time algorithm for learning parity functions in the
presence of random classification noise, a problem closely related to several cryptographic …