29 citations · 60 across the 28 of their papers we have counts for
34 papers
Deep Learning with Nonsmooth Objectives
Vinesha Peiris, Nadezda Sukhorukova, Vera Roshchina
We explore the potential for using a nonsmooth loss function based on the max-norm in the training of an artificial neural network. We hypothesise that this may lead to superior cl…
The intrinsic core and minimal faces of convex sets in general vector spaces
R. Díaz Millán, Vera Roshchina
Intrinsic core generalises the finite-dimensional notion of the relative interior to arbitrary (real) vector spaces. Our main goal is to provide a self-contained overview of the ke…
Hyperbolicity cones are amenable
Bruno F. Lourenço, Vera Roshchina, James Saunderson
Amenability is a notion of facial exposedness for convex cones that is stronger than being facially dual complete (or "nice") which is, in turn, stronger than merely being facially…
Amenable cones are particularly nice
Bruno F. Lourenço, Vera Roshchina, James Saunderson
Amenability is a geometric property of convex cones that is stronger than facial exposedness and assists in the study of error bounds for conic feasibility problems. In this paper…
Dimension and structure of higher-order Voronoi cells on discrete sites
Ryan McKewen, Vera Roshchina
We study the structure of higher-order Voronoi cells on a discrete set of sites in , focussing on the relations between cells of different order, and paying special a…
Uniqueness of solutions in multivariate Chebyshev approximation problems
Vera Roshchina, Nadia Sukhorukova, Julien Ugon
We study the solution set to multivariate Chebyshev approximation problem, focussing on the ill-posed case when the uniqueness of solutions can not be established via strict polyno…