Publications of U von Luxburg
All genres
Journal Article (11)
1.
Journal Article
17, pp. 370 - 418 (2013)
How the result of graph clustering methods depends on the construction of the graph. ESAIM: Probability and Statistics 2.
Journal Article
16, pp. 436 - 452 (2012)
How the initialization affects the stability of the қ-means algorithm. ESAIM: Probability and Statistics 3.
Journal Article
2 (3), pp. 235 - 274 (2010)
Clustering stability: an overview. Foundations and Trends in Machine Learning 4.
Journal Article
19 (3), pp. 1095 - 1117 (2009)
A Geometric Approach to Confidence Sets for Ratios: Fieller‘s Theorem, Generalizations, and Bootstrap. Statistica Sinica 5.
Journal Article
410 (19), pp. 1749 - 1764 (2009)
Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters. Theoretical computer science 6.
Journal Article
10, pp. 657 - 698 (2009)
Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions. The Journal of Machine Learning Research 7.
Journal Article
36 (2), pp. 555 - 586 (2008)
Consistency of Spectral Clustering. The Annals of Statistics 8.
Journal Article
17 (4), pp. 395 - 416 (2007)
A Tutorial on Spectral Clustering. Statistics and Computing 9.
Journal Article
8, pp. 1325 - 1370 (2007)
Graph Laplacians and their Convergence on Random Neighborhood Graphs. The Journal of Machine Learning Research 10.
Journal Article
5, pp. 669 - 695 (2004)
Distance-Based Classification with Lipschitz Functions. The Journal of Machine Learning Research 11.
Journal Article
5, pp. 293 - 323 (2004)
A Compression Approach to Support Vector Model Selection. The Journal of Machine Learning Research Book (1)
12.
Book
Statistical Learning with Similarity and Dissimilarity Functions. Logos Verlag, Berlin, Germany (2004), 166 pp.
Book Chapter (1)
13.
Book Chapter
10: Inductive Logic, pp. 651 - 706 (Eds. Gabbay, M.; Hartmann, S.; Woods, J.). Elsevier North Holland, Amsterdam, Netherlands (2011)
Statistical Learning Theory: Models, Concepts, and Results. In: Handbook of the History of Logic, Vol. Proceedings (2)
14.
Proceedings
24th Annual Conference on Learning Theory, 9-11 June 2011, Budapest, Hungary. 24th Annual Conference on Learning Theory (COLT 2011), Budapest, Hungary, July 09, 2011 - July 11, 2011. MIT Press, Cambridge, MA, USA (2011), 834 pp.
15.
Proceedings
3176). Machine Learning Summer School (MLSS 2003), Canberra, Australia, February 02, 2004 - February 14, 2004. Springer, Berlin, Germany (2004), 240 pp.
Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2 - 14, 2003, Tübingen, Germany, August 4 - 16, 2003 (Lecture Notes in Computer Science, Conference Paper (17)
16.
Conference Paper
Phase transition in the family of p-resistances. In: Advances in Neural Information Processing Systems 24, pp. 379 - 387 (Eds. Shawe-Taylor, J.; Zemel, R.; Bartlett, P.; Pereira, F.; Weinberger, K.). Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011), Granada, Spain. Curran, Red Hook, NY, USA (2012)
17.
Conference Paper
Risk-Based Generalizations of f-divergences. In: 28th International Conference on Machine Learning (ICML 2011), pp. 417 - 424 (Ed. Getoor, L.). 28th International Conference on Machine Learning (ICML 2011), Bellevue, WA, USA. International Machine Learning Society, Madison, WI, USA (2011)
18.
Conference Paper
Pruning nearest neighbor cluster trees. In: 28th International Conference on Machine Learning (ICML 2011), pp. 225 - 232 (Eds. Getoor, L.; Scheffer, T.). 28th International Conference on Machine Learning (ICML 2011), Bellevue, WA, USA. International Machine Learning Society, Madison, WI, USA (2011)
19.
Conference Paper
Getting lost in space: Large sample analysis of the resistance distance. Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS 2010), Vancouver, BC, Canada, December 06, 2010 - December 11, 2010. Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, pp. 2622 - 2630 (2011)
20.
Conference Paper
Multi-agent random walks for local clustering. In: IEEE International Conference on Data Mining (ICDM 2010), pp. 18 - 27. IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia. IEEE, Piscataway, NJ, USA (2010)