In recent years, convolutional neural networks (CNNs) have shown great performance in
various fields such as image classification, pattern recognition, and multi-media …

Support vector machine (SVM) is an optimal margin based classification technique in
machine learning. SVM is a binary linear classifier which has been extended to non-linear …

We introduce a new and increasingly relevant setting for distributed optimization in machine
learning, where the data defining the optimization are unevenly distributed over an …

Machine learning develops rapidly, which has made many theoretical breakthroughs and is
widely applied in various fields. Optimization, as an important part of machine learning, has …

Training large neural networks requires distributing learning across multiple workers, where
the cost of communicating gradients can be a significant bottleneck. signSGD alleviates this …

Coordinate descent algorithms solve optimization problems by successively performing
approximate minimization along coordinate directions or coordinate hyperplanes. They have …

In 1963, Polyak proposed a simple condition that is sufficient to show a global linear
convergence rate for gradient descent. This condition is a special case of the Łojasiewicz …

This paper contributes improvements on both the effectiveness and efficiency of Matrix
Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing …

This book, as the title suggests, is about first-order methods, namely, methods that exploit
information on values and gradients/subgradients (but not Hessians) of the functions …

We consider the problem of minimizing the sum of two convex functions: one is the average
of a large number of smooth component functions, and the other is a general convex …