5 papers
Rolled Gaussian process models for curves on manifolds
Simon Preston, Karthik Bharath, Pablo Lopez-Custodio +1
Given a planar curve, imagine rolling a sphere along that curve without slipping or twisting, and by this means tracing out a curve on the sphere. It is well known that such a roll…
Empirical likelihood for Fréchet means on open books
Karthik Bharath, Huiling Le, Andrew T A Wood +1
Empirical Likelihood (EL) is a type of nonparametric likelihood that is useful in many statistical inference problems, including confidence region construction and -sample probl…
Regression graphs and sparsity-inducing reparametrizations
Jakub Rybak, Heather Battey, Karthik Bharath
That parametrization and sparsity are inherently linked raises the possibility that relevant models, not obviously sparse in their natural formulation, exhibit a population-level s…
Sampling and estimation on manifolds using the Langevin diffusion
Karthik Bharath, Alexander Lewis, Akash Sharma +1
Error bounds are derived for sampling and estimation using a discretization of an intrinsically defined Langevin diffusion with invariant measure $\text{d}μ_ϕ\propto e^{-ϕ} \mathrm…
Non-parametric regression for robot learning on manifolds
P. C. Lopez-Custodio, K. Bharath, A. Kucukyilmaz +1
Many of the tools available for robot learning were designed for Euclidean data. However, many applications in robotics involve manifold-valued data. A common example is orientatio…