Intense recent discussions have focused on how to provide individuals with control over
when their data can and cannot be used---the EU's Right To Be Forgotten regulation is an …

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 …

This monograph covers some recent advances in a range of acceleration techniques
frequently used in convex optimization. We first use quadratic optimization problems to …

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

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 …

We improve a recent gurantee of Bach and Moulines on the linear convergence of SGD for
smooth and strongly convex objectives, reducing a quadratic dependence on the strong …

Nesterov's accelerated gradient descent (AGD), an instance of the general family of
“momentum methods,” provably achieves faster convergence rate than gradient descent …

We present an ̃O(m+n^1.5)-time randomized algorithm for maximum cardinality bipartite
matching and related problems (eg transshipment, negative-weight shortest paths, and …

Uniform sampling of training data has been commonly used in traditional stochastic
optimization algorithms such as Proximal Stochastic Mirror Descent (prox-SMD) and …

We develop a novel, fundamental, and surprisingly simple randomized iterative method for
solving consistent linear systems. Our method has six different but equivalent interpretations …