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Hierarchical penalization is a generic framework for incorporating prior information in the fitting of statistical models, when the explicative variables are organized in a hierarchical structure. The penalizer is a convex functional that performs soft sel ...
Recently, we introduced a simple variational bound on mutual information, that resolves some of the difficulties in the application of information theory to machine learning. Here we study a specific application to Gaussian channels. It is well known that ...
In this paper we will present a new coherence estimation technique for SAR interferometry products that adapts the estimation window size and shape during processing. This is of particular interest for sensors with medium spatial resolution, like the ASAR ...
We propose a highly efficient framework for kernel multi-class models with a large and structured set of classes. Kernel parameters are learned automatically by maximizing the cross-validation log likelihood, and predictive probabilities are estimated. We ...
We propose a semiparametric model for regression problems involving multiple response variables. Conditional dependencies between the responses are represented through a linear mixture of Gaussian processes. We propose an efficient approximate inference sc ...
Reconstruction method and devices for two-dimensional signals that are not bandlimited but have a parametric representation with a finite number of degrees of freedom. The signal is reconstructed from the samples obtained after a suitable filtering with a ...
Hierarchical penalization is a generic framework for incorporating prior information in the fitting of statistical models, when the explicative variables are organized in a hierarchical structure. The penalizer is a convex functional that performs soft sel ...
The estimation of cumulative distributions is classically performed using the empirical distribution function. This estimator has excellent properties but is lacking continuity. Smooth versions of the empirical distribution function have been obtained by k ...
We define multi-scale moments that are estimated locally by analyzing the image through a sliding window at multiple scales. When the analysis window satisfies a two-scale relation, we prove that these moments can be computed very efficiently using a multi ...
The problem of reconstructing an unknown signal from n noisy samples can be addressed by means of non- parametric estimation techniques such as Tikhonov reg- ularization, Bayesian regression and state-space fixed- interval smoothing. The practical use of t ...
