Parkinson's Disease (PD) is a progressive neurodegenerative disorder that significantly
impacts both motor and non-motor functions, including speech. Early and accurate …

Stochastic and finite-sum optimization problems are central to machine learning. Numerous
specializations of these problems involve nonlinear constraints where the parameters of …

Adaptive stochastic gradient algorithms in the Euclidean space have attracted much
attention lately. Such explorations on Riemannian manifolds, on the other hand, are …

We consider the problem of decentralized nonconvex optimization over a compact
submanifold, where each local agent's objective function defined by the local dataset is …

The key problem of multi-view clustering is to handle the inconsistency among multiple
views. This article proposes an attention-based framework for multi-view clustering on …

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

One of the popular approaches for low-rank tensor completion is to use the latent trace norm
regularization. However, most existing works in this direction learn a sparse combination of …

To assist the clinical diagnosis and treatment of speech dysarthria, automatic dysarthric
speech detection techniques providing reliable and cost-effective assessment are …

In this work, we consider the bilevel optimization problem on Riemannian manifolds. We
inspect the calculation of the hypergradient of such problems on general manifolds and thus …

In this paper, we focus on subspace learning problems on the Grassmann manifold.
Interesting applications in this setting include low-rank matrix completion and low …