5 citations · 5 across the 4 of their papers we have counts for
4 papers
Linear system identification using stable spline kernels and PLQ penalties
Aleksandr Y. Aravkin, James V. Burke, Gianluigi Pillonetto
The classical approach to linear system identification is given by parametric Prediction Error Methods (PEM). In this context, model complexity is often unknown so that a model ord…
Optimization viewpoint on Kalman smoothing, with applications to robust and sparse estimation
Aleksandr Y. Aravkin, James V. Burke, Gianluigi Pillonetto
In this paper, we present the optimization formulation of the Kalman filtering and smoothing problems, and use this perspective to develop a variety of extensions and applications.…
Convex vs nonconvex approaches for sparse estimation: GLasso, Multiple Kernel Learning and Hyperparameter GLasso
Aleksandr Y. Aravkin, James V. Burke, Alessandro Chiuso +1
The popular Lasso approach for sparse estimation can be derived via marginalization of a joint density associated with a particular stochastic model. A different marginalization of…
Epi-convergent Smoothing with Applications to Convex Composite Functions
James V. Burke, Tim Hoheisel
Smoothing methods have become part of the standard tool set for the study and solution of nondifferentiable and constrained optimization problems as well as a range of other variat…