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stat.ML2013
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…
stat.ML2013★ 5 cited
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…