This paper surveys quantum learning theory: the theoretical aspects of machine learning
using quantum computers. We describe the main results known for three models of learning …

Machine learning (ML) models may be deemed confidential due to their sensitive training
data, commercial value, or use in security applications. Increasingly often, confidential ML …

Boolean functions are perhaps the most basic objects of study in theoretical computer
science. They also arise in other areas of mathematics, including combinatorics, statistical …

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 state and analyze the first active learning algorithm which works in the presence of
arbitrary forms of noise. The algorithm, A 2 (for Agnostic Active), relies only upon the …

We give a computationally efficient algorithm that learns (under distributional assumptions) a
halfspace in the difficult agnostic framework of Kearns, Schapire, and Sellie Mach. Learn …

From the winner of the Turing Award and the Abel Prize, an introduction to computational
complexity theory, its connections and interactions with mathematics, and its central role in …

Active learners alleviate the burden of labeling large amounts of data by detecting and
asking the user to label only the most informative examples in the domain. We focus here on …

We analyze the performance of top-down algorithms for decision tree learning, such asthose
employed by the widely used C4. 5 and CART software packages. Our main result is aproof …

Formal synthesis is the process of generating a program satisfying a high-level formal
specification. In recent times, effective formal synthesis methods have been proposed based …