In this work, we propose a hierarchical latent dictionary approach to estimate the timevarying mean and covariance of a process for which we have only limited noisy samples. We fu...
Alona Fyshe, Emily B. Fox, David B. Dunson, Tom M....
Often, high dimensional data lie close to a low-dimensional submanifold and it is of interest to understand the geometry of these submanifolds. The homology groups of a manifold a...
Sivaraman Balakrishnan, Alessandro Rinaldo, Don Sh...
A nonparametric version of the basis pursuit method is developed for field estimation. The underlying model entails known bases, weighted by generic functions to be estimated fro...
Many real-world data sets can be viewed of as noisy samples of special types of metric spaces called metric graphs [16]. Building on the notions of correspondence and GromovHausdo...