Visitor

Office: 206

Spemannstr. 38

72076 Tübingen

Spemannstr. 38

72076 Tübingen

I am a professor for the theory of machine learning at the Department of Computer Science, University of Tuebingen. I spend about one day a week at the MPI to collaborate with the people there.

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)