Variational inference (VI) seeks to approximate a target distribution $\pi $ by an element of a
tractable family of distributions. Of key interest in statistics and machine learning is Gaussian …

As science and engineering have become increasingly data-driven, the role of optimization
has expanded to touch almost every stage of the data analysis pipeline, from signal and …

Projection robust Wasserstein (PRW) distance, or Wasserstein projection pursuit (WPP), is a
robust variant of the Wasserstein distance. Recent work suggests that this quantity is more …

Many contemporary applications in signal processing and machine learning give rise to
structured nonconvex nonsmooth optimization problems that can often be tackled by simple …

We consider a distributed non-convex optimization where a network of agents aims at
minimizing a global function over the Stiefel manifold. The global function is represented as …

From optimal transport to robust dimensionality reduction, many machine learning
applicationscan be cast into the min-max optimization problems over Riemannian manifolds …

We consider a class of Riemannian optimization problems where the objective is the sum of
a smooth function and a nonsmooth function, considered in the ambient space. This class of …

This paper is about the old Wahba problem in its more general form, which we call"
simultaneous rotation and correspondence search". In this generalization we need to find a …

In this paper, we study the generalized subdifferentials and the Riemannian gradient
subconsistency that are the basis for non-Lipschitz optimization on embedded submanifolds …

In recent years, the proximal gradient method and its variants have been generalized to
Riemannian manifolds for solving optimization problems with an additively separable …