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Christian Igel

Professor, Dr. habil.
Office address:
University of Copenhagen
Department of Computer Science
Sigurdsgade 41
2200 København N

Mail address:
University of Copenhagen
Department of Computer Science
Universitetsparken 5
2100 København Ø
Phone: (+45) 21849673

Short CV

I studied Computer Science at the Technical University of Dortmund, Germany. In 2002, I received my Doctoral degree from the Faculty of Technology, Bielefeld University, Germany, and in 2010 my Habilitation degree from the Department of Electrical Engineering and Information Sciences, Ruhr-University Bochum, Germany. From 2003 to 2010, I was a Juniorprofessor for Optimization of Adaptive Systems at the Institut für Neuroinformatik, Ruhr-University Bochum. In October 2010, I was appointed professor with special duties in machine learning at DIKU. Since December 2014 I am full professor at DIKU.

Research Interests

My main research area is Machine Learning.

Currently I am particularly interested in

  • support vector machines and other kernel-based methods,
  • evolution strategies for single- and multi-objective optimization and reinforcement learning,
  • deep neural networks and stochastic neural networks,
and applications of these methods.

Selected Publications

Please click here for a full list. I also maintain a Google scholar profile.

Oswin Krause, Dídac R. Arbonès, and Christian Igel. CMA-ES with Optimal Covariance Update and Storage Complexity. Advances in Neural Information Processing Systems (NIPS), supplement, 2016
Ürün Dogan, Tobias Glasmachers, and Christian Igel. A Unified View on Multi-class Support Vector Classification. Journal of Machine Learning Research 17(45), pp. 1-32, 2016
Fabian Gieseke, Justin Heinermann, Cosmin Oancea, and Christian Igel. Buffer k-d Trees: Processing Massive Nearest Neighbor Queries on GPUs. Proceedings of the 31st International Conference on Machine Learning (ICML). JMLR W&CP 32(1), pp. 172-180, 2014
Kai Brügge, Asja Fischer, and Christian Igel. The flip-the-state transition operator for restricted Boltzmann machines. Machine Learning 13, pp. 53-69, 2013
Oswin Krause, Asja Fischer, Tobias Glasmachers, and Christian Igel. Approximation properties of DBNs with binary hidden units and real-valued visible units. Proceedings of the 30st International Conference on Machine Learning (ICML). JMLR W&CP 28(1), pp. 419–426, 2013
Kim Steenstrup Pedersen, Kristoffer Stensbo-Smidt, Andrew Zirm, and Christian Igel. Shape Index Descriptors Applied to Texture-Based Galaxy Analysis. International Conference on Computer Vision (ICCV), pp. 2440-2447, IEEE Press, 2013
Asja Fischer and Christian Igel. Bounding the Bias of Contrastive Divergence Learning. Neural Computation 23, pp. 664-673, 2011
Tobias Glasmachers and Christian Igel. Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(8), pp. 1522-1528, 2010 source code
Thorsten Suttorp, Nikolaus Hansen, and Christian Igel. Efficient Covariance Matrix Update for Variable Metric Evolution Strategies. Machine Learning 75, pp. 167-197, 2009 source code
Verena Heidrich-Meisner and Christian Igel. Hoeffding and Bernstein Races for Selecting Policies in Evolutionary Direct Policy Search. In L. Bottou and M. Littman, eds.: Proceedings of the International Conference on Machine Learning (ICML 2009), pp. 401-408, 2009
Christian Igel, Verena Heidrich-Meisner, and Tobias Glasmachers. Shark. Journal of Machine Learning Research 9, pp. 993-996, 2008 source code
Tobias Glasmachers and Christian Igel. Maximum-Gain Working Set Selection for SVMs. Journal of Machine Learning Research 7, pp. 1437-1466, 2006 source code