One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

A vast majority of machine learning algorithms train their models and perform inference by
solving optimization problems. In order to capture the learning and prediction problems …

Abstract Sparsity in Deep Neural Networks (DNNs) is studied extensively with the focus of
maximizing prediction accuracy given an overall parameter budget. Existing methods rely on …

This paper develops a novel tree-based algorithm, called Bonsai, for efficient prediction on
IoT devices–such as those based on the Arduino Uno board having an 8 bit ATmega328P …

The cost of privacy: Optimal rates of convergence for parameter estimation with differential
privacy Page 1 The Annals of Statistics 2021, Vol. 49, No. 5, 2825–2850 https://doi.org/10.1214/21-AOS2058 …

We investigate the performance of the standard Greedy algorithm for cardinality constrained
maximization of non-submodular nondecreasing set functions. While there are strong …

Praise for the first edition:"[This book] succeeds singularly at providing a structured
introduction to this active field of research.… it is arguably the most accessible overview yet …

Optimization problems with rank constraints arise in many applications, including matrix
regression, structured PCA, matrix completion and matrix decomposition problems. An …

Several real-world applications require real-time prediction on resource-scarce devices
such as an Internet of Things (IoT) sensor. Such applications demand prediction models with …

This paper considers the noisy sparse phase retrieval problem: recovering a sparse signal
x∈R^p from noisy quadratic measurements y_j=(a_j'x)^2+j, j=1,...,m, with independent sub …