Max Planck Research Group Leader

Office: S2.001

Spemannstr. 34

72076 Tübingen

Germany

Spemannstr. 34

72076 Tübingen

Germany

+49 7071 29 75945

+49 7071 601 552

I am a professor for the theory of machine learning at the Department of Computer Science, University of Tuebingen, and a fellow at the MPI for Intelligent Systems.

For more information please check my webpage at the university.

More information about me can be found on my university webpage.

More information about me can be found on my university webpage.

39 results
(BibTeX)

**Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines**
*Proceedings of the 3rd ACM conference on Learning @ Scale*, pages: 369-378, (Editors: Haywood, J. and Aleven, V. and Kay, J. and Roll, I.), ACM, L@S, 2016, (An earlier version of this paper had been presented at the ICML 2015 workshop for Machine Learning for Education.) (conference)

**How the result of graph clustering methods depends on the construction of the graph**
*ESAIM: Probability & Statistics*, 17, pages: 370-418, January 2013 (article)

**Density estimation from unweighted k-nearest neighbor graphs: a roadmap**
In *Advances in Neural Information Processing Systems 26*, pages: 225-233, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

**Clustering: Science or Art?**
In *JMLR Workshop and Conference Proceedings, Volume 27*, pages: 65-79, Workshop on Unsupervised Learning and Transfer Learning, 2012 (inproceedings)

**How the initialization affects the stability of the k-means algorithm
**
*ESAIM: Probability and Statistics*, 16, pages: 436-452, January 2012 (article)

**Shortest path distance in random k-nearest neighbor graphs**
In *Proceedings of the 29th International Conference on Machine Learning*, International Machine Learning Society, International Conference on Machine Learning (ICML), 2012 (inproceedings)

**Phase transition in the family of p-resistances**
In *Advances in Neural Information Processing Systems 24*, pages: 379-387, (Editors: J Shawe-Taylor and RS Zemel and P Bartlett and F Pereira and KQ Weinberger), Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS), 2011 (inproceedings)

**Statistical Learning Theory: Models, Concepts, and Results**
In *Handbook of the History of Logic, Vol. 10: Inductive Logic*, 10, pages: 651-706, (Editors: Gabbay, D. M., Hartmann, S. and Woods, J. H.), Elsevier North Holland, Amsterdam, Netherlands, May 2011 (inbook)

**Pruning nearest neighbor cluster trees**
In pages: 225-232, (Editors: Getoor, L. , T. Scheffer), International Machine Learning Society, Madison, WI, USA, 28th International Conference on Machine Learning (ICML), July 2011 (inproceedings)

**Risk-Based Generalizations of f-divergences**
In pages: 417-424, (Editors: Getoor, L. , T. Scheffer), International Machine Learning Society, Madison, WI, USA, 28th International Conference on Machine Learning (ICML), July 2011 (inproceedings)

**JMLR Workshop and Conference Proceedings Volume 19: COLT 2011**
pages: 834, MIT Press, Cambridge, MA, USA, 24th Annual Conference on Learning Theory , June 2011 (proceedings)

**Getting lost in space: Large sample analysis of the resistance distance**
In *Advances in Neural Information Processing Systems 23*, pages: 2622-2630, (Editors: Lafferty, J. , C. K.I. Williams, J. Shawe-Taylor, R. S. Zemel, A. Culotta), Curran, Red Hook, NY, USA, Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS), 2010 (inproceedings)

**Clustering stability: an overview**
*Foundations and Trends in Machine Learning*, 2(3):235-274, July 2010 (article)

**Multi-agent random walks for local clustering**
In *Proceedings of the IEEE International Conference on Data Mining (ICDM 2010)*, pages: 18-27, (Editors: Webb, G. I., B. Liu, C. Zhang, D. Gunopulos, X. Wu), IEEE, Piscataway, NJ, USA, IEEE International Conference on Data Mining (ICDM), December 2010 (inproceedings)

**Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions**
*Journal of Machine Learning Research*, 10, pages: 657-698, March 2009 (article)

**Influence of graph construction on graph-based clustering measures**
In *Advances in neural information processing systems 21*, pages: 1025-1032, (Editors: Koller, D. , D. Schuurmans, Y. Bengio, L. Bottou), Curran, Red Hook, NY, USA, Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS), June 2009 (inproceedings)

**Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters**
*Theoretical Computer Science*, 410(19):1749-1764, April 2009 (article)

**Generalized Clustering via Kernel Embeddings**
In *KI 2009: AI and Automation, Lecture Notes in Computer Science, Vol. 5803*, pages: 144-152, (Editors: B Mertsching and M Hund and Z Aziz), Springer, Berlin, Germany, 32nd Annual Conference on Artificial Intelligence (KI), September 2009 (inproceedings)

**A Geometric Approach to Confidence Sets for Ratios: Fieller’s Theorem, Generalizations, and Bootstrap**
*Statistica Sinica*, 19(3):1095-1117, July 2009 (article)

**Relating clustering stability to properties of cluster boundaries**
In *COLT 2008*, pages: 379-390, (Editors: Servedio, R. A., T. Zhang), Omnipress, Madison, WI, USA, 21st Annual Conference on Learning Theory, July 2008 (inproceedings)

**Consistent Minimization of Clustering Objective Functions**
In *Advances in neural information processing systems 20*, pages: 961-968, (Editors: Platt, J. C., D. Koller, Y. Singer, S. Roweis), Curran, Red Hook, NY, USA, Twenty-First Annual Conference on Neural Information Processing Systems (NIPS), September 2008 (inproceedings)

**Consistency of Spectral Clustering**
*Annals of Statistics*, 36(2):555-586, April 2008 (article)

**Graph Laplacians and their Convergence on Random Neighborhood Graphs**
*Journal of Machine Learning Research*, 8, pages: 1325-1370, June 2007 (article)

**A Tutorial on Spectral Clustering**
*Statistics and Computing*, 17(4):395-416, December 2007 (article)

**Cluster Identification in Nearest-Neighbor Graphs**
In *ALT 2007*, pages: 196-210, (Editors: Hutter, M. , R. A. Servedio, E. Takimoto), Springer, Berlin, Germany, 18th International Conference on Algorithmic Learning Theory, October 2007 (inproceedings)

**Cluster Identification in Nearest-Neighbor Graphs**
(163), Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany, May 2007 (techreport)

**A tutorial on spectral clustering**
(149), Max Planck Institute for Biological Cybernetics, Tübingen, August 2006 (techreport)

**A Sober Look at Clustering Stability**
In *COLT 2006*, pages: 5-19, (Editors: Lugosi, G. , H.-U. Simon), Springer, Berlin, Germany, 19th Annual Conference on Learning Theory, September 2006 (inproceedings)

**From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians**
In *Proceedings of the 18th Conference on Learning Theory (COLT)*, pages: 470-485, Conference on Learning Theory, 2005, Student Paper Award (inproceedings)

**Limits of Spectral Clustering**
In *Advances in Neural Information Processing Systems 17*, pages: 857-864, (Editors: Saul, L. K., Y. Weiss, L. Bottou), MIT Press, Cambridge, MA, USA, Eighteenth Annual Conference on Neural Information Processing Systems (NIPS), July 2005 (inproceedings)

**Towards a Statistical Theory of Clustering. Presented at the PASCAL workshop on clustering, London**
Presented at the PASCAL workshop on clustering, London, 2005 (techreport)

**Advanced Lectures on Machine Learning**
*ML Summer Schools 2003*, LNAI 3176, pages: 240, Springer, Berlin, Germany, ML Summer Schools, September 2004 (proceedings)

**A Compression Approach to Support Vector Model Selection**
*Journal of Machine Learning Research*, 5, pages: 293-323, April 2004 (article)

**Statistical Learning with Similarity and Dissimilarity Functions**
pages: 1-166, Technische Universität Berlin, Germany, Technische Universität Berlin, Germany, 2004 (phdthesis)

**Distance-Based Classification with Lipschitz Functions**
*Journal of Machine Learning Research*, 5, pages: 669-695, June 2004 (article)

**On the Convergence of Spectral Clustering on Random Samples: The Normalized Case**
In *Proceedings of the 17th Annual Conference on Learning Theory*, pages: 457-471, Proceedings of the 17th Annual Conference on Learning Theory, 2004 (inproceedings)

**Confidence Sets for Ratios: A Purely Geometric Approach To Fieller’s Theorem**
(133), Max Planck Institute for Biological Cybernetics, 2004 (techreport)

**Distance-based classification with Lipschitz functions**
In *Learning Theory and Kernel Machines, Proceedings of the 16th Annual Conference on Computational Learning Theory*, pages: 314-328, (Editors: Schölkopf, B. and M.K. Warmuth), Learning Theory and Kernel Machines, Proceedings of the 16th Annual Conference on Computational Learning Theory, 2003 (inproceedings)

**A compression approach to support vector model selection**
(101), Max Planck Institute for Biological Cybernetics, 2002, see more detailed JMLR version (techreport)