Sparse estimation (or sparse recovery) is playing an increasingly important role in the statistics and machine learning communities. Several methods ...

In making advances within Computational Systems Biology there is an acknowledged need for the ongoing development of both probabilistic and ...

The Summer Schools in Logic and Learning bring together two annual summer schools in the area of logic and machine ...

Kernel methods are widely used to address a variety of learning tasks including classification, regression, ranking, clustering, and dimensionality reduction. ...

While the machine learning community has primarily focused on analysing the output of a single data source, there has been ...

Classical optimization techniques have found widespread use in machine learning. Convex optimization has occupied the center-stage and significant effort continues ...

This workshop aims at collecting theoretical insights in the design of data-dependent learning strategies. Specifically we are interested in how ...

Machine learning has traditionally been focused on prediction. Given observations that have been generated by an unknown stochastic dependency, the ...

The field of computational biology has seen dramatic growth over the past few years, both in terms of new available ...

We believe that the wide-spread adoption of open source software policies will have a tremendous impact on the field of ...

Structured data emerges rapidly in a large number of disciplines: bioinformatics, systems biology, social network analysis, natural language processing and ...

There has recently been a surge of interest in algebraic methods in machine learning. In no particular order, this includes: ...