Prof. Dr. Marius Kloft




Humboldt University of Berlin
Department of Computer Science
Rudower Chaussee 25
Room 4.215
12489 Berlin

☎ +49 30 2093 3027

[ Bio | Research | Teaching | Publications | Activities ]

Short Bio

    Since 04/2014 I am a junior professor at the Department of Computer Science of Humboldt University of Berlin. Prior to joining HU Berlin I was a joint postdoctoral fellow at the Courant Institute of Mathematical Sciences and Memorial Sloan-Kettering Cancer Center, New York, working with Mehryar Mohri and Gunnar Rätsch, respectively. From 2007-2011, I was a PhD student in the machine learning program of TU Berlin, headed by Klaus-Robert Müller. I was co-advised by Gilles Blanchard and Peter L. Bartlett, whose learning theory group at UC Berkeley I visited from 10/2009 to 10/2010. In 2006, I received a diploma (MSc equivalent) in mathematics from the University of Marburg with a thesis in algebraic geometry.

Research Interests

    I am interested in statistical machine learning and its applications, in particular, computational biology. For instance, I have been working on several aspects of multiple kernel learning: (non-sparse) regularization strategies, generalization bounds, unified framework, and novelty detection. I have co-organized workshops on new directions in multiple kernel learning and multi-task and transfer learning at NIPS 2010 and 2013, respectively. My dissertation on Lp-norm multiple kernel learning was nominated by TU Berlin for the Doctoral Dissertation Award of the German Chapter of the ACM (GI).

Teaching


Publications

    2014

    2013

  • C. Cortes, M. Kloft, M. Mohri. Learning Kernels Using Local Rademacher Complexity.
    Advances in Neural Information Processing Systems (NIPS) 26, (to appear) 2013.
    Spotlight paper (52 spotlights out of 1420 submissions). Google Most Influential Papers 2013 Award.

    2012

    2011

  • M. Kloft. Lp-Norm Multiple Kernel Learning.
    Dissertation, Berlin Institute of Technology (TU Berlin), Oct 2011.
    Finalist - Doctoral Dissertation Award of the German Chapter of the ACM.

    2010

    2009

  • A. Binder, M. Kawanabe, M. Kloft, and S. Nakajima. Enhancing Image Annotation with Primitive Color Histograms via Non-sparse Multiple Kernel Learning. NIPS Workshop on Understanding Multiple Kernel Learning Methods, 2009.
  • M. Kloft, U. Brefeld, S. Sonnenburg, A. Zien, P. Laskov, and K.-R. Müller. Learning Non-sparse Kernel Mixtures.
    Proceedings of the PASCAL2 Workshop on Sparsity in Machine Learning and Statistics, 2009.

    2008 and before

  • M. Kloft, U. Brefeld, P. Laskov, and S. Sonnenburg. Non-sparse Multiple Kernel Learning.
    Proceedings of the NIPS Workshop on Kernel Learning: Automatic Selection of Optimal Kernels, 2008.


Activities


2013
Workshop Organization: NIPS: Area co-chairing (w G. Rätsch): NIPS: Program Committee: ACML, ECML/PKDD, NCVPRIPG. Reviewer: AISTATS, Bioinformatics, Biosystems, EJS, ICML, IJA, IEEE IT, IJITDM, JMLR, Math Rev, MLCB, NEPL, NEUCOM, NIPS, PloS Comp Bio, PLoS ONE, SPM, TKDE, TNNLS, TPAMI, TSMCB.
2012
Program Committee: ECML/PKDD. ICPR; Reviewer: COLT, DKE, EJS, ICVGIP, JMLR, JCST, NEPL, NEUCOM, NEUNET, NIPS, OPT2012, PR, SMCB, SPM, TNNLS, TPAMI.
2011
Program Committee: IJCAI; Reviewer: COLT, DAGM, ICML, JCST, JMLR, KAIS, MLJ, NEPL, NIPS, TNN, TPAMI, ZUSC.
2010
Workshop Organization: NIPS; Program Committee: AISEC, ECML/PKDD, ICPR; Reviewer: BInf, CSDA, ICML, JMLR, MLJ, NEPL, NIPS, TNN, TPAMI.
2009
Program Committee: AISEC; Reviewer: TPAMI, TNN, PR, ECML/PKDD, NIPS, ICML, AISTATS.
2008
Reviewer: TPAMI, NEUCOM, AAAI, PAKDD, ECML/PKDD, NIPS.
2007
Reviewer: NIPS, PAKDD.


Acknowledgements / Collaborators





*The original publication is available at http://www.acm.org
**The original publication is available at http://www.springerlink.com
***The original publication is available at http://ieeexplore.ieee.org
****The original publication is available at http://journals.cambridge.org
*****The original publication is available at http://www.plosone.org