Kernel-based Machine Learning from Multiple Information Sources

In my talk I will introduce multiple kernel learning, a machine learning framework for integrating multiple types of representation into the learning process. Furthermore I will present an extension called multi-task multiple kernel learning, which can be used for effectively learning from multiple sources of information, even when the relations between the sources are completely unknown. The applicability of the methodology is illustrated by applications taken from the domains of visual object recognition and computational biology.

Bio

Since 2014 Marius Kloft is a junior professor of machine learning at the Department of Computer Science of Humboldt University of Berlin, where he is since 2015 also leading the Emmy-Noether research group on statistical learning from dependent data. Prior to joining HU Berlin he was a joint postdoctoral fellow at the Courant Institute of Mathematical Sciences and Memorial Sloan-Kettering Cancer Center, New York, working with Mehryar Mohri, Corinna Cortes, and Gunnar Rätsch. From 2007-2011, he was a PhD student in the machine learning program of TU Berlin, headed by Klaus-Robert Müller. He was co-advised by Gilles Blanchard and Peter L. Bartlett, whose learning theory group at UC Berkeley he visited from 10/2009 to 10/2010. In 2006, he received a diploma (MSc equivalent) in mathematics from the University of Marburg with a thesis in algebraic geometry.

Marius Kloft is interested in statistical machine learning methods for analysis of large amounts of data as well as applications, in particular, computational biology. Together with colleagues he has developed learning methods for integrating the information from multiple sensor types (multiple kernel learning) or multiple learning tasks (transfer learning), which have successfully been applied in various application domains, including network intrusion detection (REMIND system), visual image recognition (1st place at ImageCLEF Visual Object Recognition Challenge), computational personalized medicine (1st place at NCI-DREAM Drug Sensitivity Prediction Challenge), and computational genomics (most accurate gene start detector in international comparison of 19 leading models). For his research, Marius Kloft received the Google Most Influential Papers 2013 award.