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 …

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 …

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

We consider stochastic zeroth-order optimization over Riemannian submanifolds embedded
in Euclidean space, where the task is to solve Riemannian optimization problems with only …

Clustering is a key technique in segmenting data into different groups of similar
observations. As clustering is an unsupervised learning method, the latent cluster …

Riemannian optimization has drawn a lot of attention due to its wide applications in practice.
Riemannian stochastic first-order algorithms have been studied in the literature to solve …

We consider optimization problems over the Stiefel manifold whose objective function is the
summation of a smooth function and a nonsmooth function. Existing methods for solving this …

Motor imagery (MI) classification is key for brain-computer interfaces (BCIs). Until recent
years, numerous models had been proposed, ranging from classical algorithms like …

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 …

We study a principal component analysis problem under the spiked Wishart model in which
the structure in the signal is captured by a class of union-of-subspace models. This general …