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Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks
Mario Marchand · Hongyu Su · Emilie Morvant · Juho Rousu · John Shawe-Taylor

Tue Dec 09 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D

We show that the usual score function for conditional Markov networks can be written as the expectation over the scores of their spanning trees. We also show that a small random sample of these output trees can attain a significant fraction of the margin obtained by the complete graph and we provide conditions under which we can perform tractable inference. The experimental results confirm that practical learning is scalable to realistic datasets using this approach.

Author Information

Mario Marchand (Université Laval)
Hongyu Su (Aalto University)
Emilie Morvant (LaHC, University of Saint-Etienne)
Juho Rousu (Aalto University)
John Shawe-Taylor (UCL)

John Shawe-Taylor has contributed to fields ranging from graph theory through cryptography to statistical learning theory and its applications. However, his main contributions have been in the development of the analysis and subsequent algorithmic definition of principled machine learning algorithms founded in statistical learning theory. This work has helped to drive a fundamental rebirth in the field of machine learning with the introduction of kernel methods and support vector machines, driving the mapping of these approaches onto novel domains including work in computer vision, document classification, and applications in biology and medicine focussed on brain scan, immunity and proteome analysis. He has published over 300 papers and two books that have together attracted over 60000 citations. He has also been instrumental in assembling a series of influential European Networks of Excellence. The scientific coordination of these projects has influenced a generation of researchers and promoted the widespread uptake of machine learning in both science and industry that we are currently witnessing.

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