We develop regression diagnostics for functional regression models which relate a functional response to predictor variables that can be multivariate vectors or random functions. ...
We address an approximation method for Gaussian process (GP) regression, where we approximate covariance by a block matrix such that diagonal blocks are calculated exactly while o...
Most state-of-the-art nonrigid shape recovery methods
usually use explicit deformable mesh models to regularize
surface deformation and constrain the search space. These
triangu...
We study the problem of discovering a manifold that best preserves information relevant to a nonlinear regression. Solving this problem involves extending and uniting two threads ...
In this work we present and discuss in detail a novel vector-valued regression technique: our approach allows for an all-at-once estimation, as opposed to solve a number of scalar...
Luca Baldassarre, Annalisa Barla, Barbara Gianesin...