The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...
Automated generators for synthetic models and data can play a crucial role in designing new algorithms/modelframeworks, given the sparsity of benchmark models for empirical analys...
We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to...
Onureena Banerjee, Laurent El Ghaoui, Alexandre d'...
Background: Various high throughput methods are available for detecting regulations at the level of transcription, translation or posttranslation (e.g. phosphorylation). Integrati...
Martin Klammer, Klaus Godl, Andreas Tebbe, Christo...
We describe and evaluate a new, pipelined algorithm for large, irregular all-gather problems. In the irregular all-gather problem each process in a set of processes contributes in...