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Christian Igel

Professor, Dr. habil.
Office address:    Mail address:
Department of Computer Science    Department of Computer Science
University of Copenhagen    University of Copenhagen
Universitetsparken 1, room 1.2.20    Universitetsparken 5
2100 København Ø    2100 København Ø

Email: igel@diku.dk
Phone: (+45) 21849673

Short CV

I am professor at DIKU, the Department of Computer Science at the University of Copenhagen. 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 a full professor at DIKU.

Among others, I serve as an Editor of the German Journal on Artificial Intelligence (KI) and an Associate Editor of the Evolutionary Computation Journal (ECJ) and the Artificial Intelligence Journal (AIJ).

I am director of the SCIENCE AI Centre.

Research Interests

My main research area is Machine Learning.

Currently, I am particularly interested in

and applications of these methods.

Selected Publications

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

Fabian Gieseke and Christian Igel. Training Big Random Forests with Little Resources. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 1445-1454, ACM Press, 2018
Jan Kremer, Fei Sha, and Christian Igel. Robust Active Label Correction. International Conference on Artificial Intelligence and Statistics (AISTATS), Proceedings of Machine Learning Research 84, 2018
Niklas Thiemann, Christian Igel, Olivier Wintenberger, and Yevgeny Seldin. A Strongly Quasiconvex PAC-Bayesian Bound. In S. Hanneke and L. Reyzin, eds.: Algorithmic Learning Theory (ALT), Proceedings of Machine Learning Research 76, pp. 466-492, 2017
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
Søren Frejstrup Maibing and Christian Igel. Computational Complexity of Linear Large Margin Classification With Ramp Loss. JMLR W&CP 38 (AISTATS), pp. 259-267, 2015
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 source code
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
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