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.
slt
Tang, C., Garreau, D., von Luxburg, U.
When do random forests fail?
In Proceedings Neural Information Processing Systems, Neural Information Processing Systems (NIPS 2018) , December 2018 (inproceedings)
slt
Haghiri, S., Garreau, D., Luxburg, U. V.
Comparison-Based Random Forests
International Conference on Machine learning (ICML), 2018 (conference)
ei
slt
Shah*, N., Tabibian*, B., Muandet, K., Guyon, I., von Luxburg, U.
Design and Analysis of the NIPS 2016 Review Process
Journal of Machine Learning Research, 19(49):1-34, 2018, *equal contribution (article)
slt
Ghoshdastidar, D., Perrot, M., von Luxburg, U.
Foundations of Comparison-Based Hierarchical Clustering
2018, arXiv preprint (arXiv:1811.00928) (article)
slt
Vankadara, L., von Luxburg, U.
Measures of distortion for machine learning
In Proceedings Neural Information Processing Systems, Neural Information Processing Systems (NIPS 2018) , 2018 (inproceedings)
slt
Ghoshdastidar, D., von Luxburg, U.
Practical Methods for Graph Two-Sample Testing
In Proceedings Neural Information Processing Systems, Neural Information Processing Systems (NIPS 2018) , 2018 (inproceedings)
slt
Perrot, M., von Luxburg, U.
Boosting for Comparison-Based Learning
2018, arXiv preprint (arXiv:1810.13333) (article)
slt
Haghiri, S., Ghoshdastidar, D., von Luxburg, U.
Comparison-based nearest neighbor search
In Artificial Intelligence and Statistics, Artificial Intelligence and Statistics (AISTATS), 2017 (inproceedings)
slt
Kleindessner, M., von Luxburg, U.
Kernel functions based on triplet comparisons
In Proceedings Neural Information Processing Systems, Neural Information Processing Systems (NIPS), 2017 (inproceedings)
slt
Ghoshdastidar, D., Gutzeit, M., Carpentier, A., von Luxburg, U.
Two-sample Hypothesis Testing for Inhomogeneous Random Graphs
2017, arXiv preprint (arXiv:1707.00833) (article)
slt
Kleindessner, M., von Luxburg, U.
Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis
Journal of Machine Learning Research (JMLR), Journal of Machine Learning Research, 18, 2017 (article)
slt
Ghoshdastidar, D., Gutzeit, M., Carpentier, A., von Luxburg, U.
Two-sample tests for large random graphs using network statistics
In Conference on Computational Learning Theory (COLT), Conference on Computational Learning Theory (COLT), 2017 (inproceedings)
ei
Sajjadi, M. S. M., Alamgir, M., von Luxburg, U.
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, April 2016, (An earlier version of this paper had been presented at the ICML 2015 workshop for Machine Learning for Education.) (conference)
ei
Sajjadi, M. S. M., Alamgir, M., von Luxburg, U.
Peer grading in a course on algorithms and data structures
Workshop on Machine Learning for Education (ML4Ed) at the 32th International Conference on Machine Learning (ICML), 2015 (conference)
ei
Sajjadi, M. S. M., Alamgir, M., von Luxburg, U.
Peer grading in a course on algorithms and data structures
Workshop on Crowdsourcing and Machine Learning (CrowdML) Workshop on Machine Learning for Education (ML4Ed) at at the 32th International Conference on Machine Learning (ICML), 2015 (conference)
ei
Maier, M., von Luxburg, U., Hein, M.
How the result of graph clustering methods depends on the construction of the graph
ESAIM: Probability & Statistics, 17, pages: 370-418, January 2013 (article)
ei
von Luxburg, U., Alamgir, M.
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)
ei
Bubeck, S., Meila, M., von Luxburg, U.
How the initialization affects the stability of the k-means algorithm
ESAIM: Probability and Statistics, 16, pages: 436-452, January 2012 (article)
ei
von Luxburg, U., Williamson, R., Guyon, I.
Clustering: Science or Art?
In JMLR Workshop and Conference Proceedings, Volume 27, pages: 65-79, Workshop on Unsupervised Learning and Transfer Learning, 2012 (inproceedings)
ei
Alamgir, M., von Luxburg, U.
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)
ei
Kpotufe, S., von Luxburg, U.
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)
ei
García-García, D., von Luxburg, U., Santos-Rodríguez, R.
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)
ei
Kakade, S., von Luxburg, U.
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)
ei
von Luxburg, U., Schölkopf, B.
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)
ei
Alamgir, M., von Luxburg, U.
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)
ei
Alamgir, M., von Luxburg, U.
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)
ei
von Luxburg, U.
Clustering stability: an overview
Foundations and Trends in Machine Learning, 2(3):235-274, July 2010 (article)
ei
von Luxburg, U., Radl, A., Hein, M.
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)
ei
Jegelka, S., Gretton, A., Schölkopf, B., Sriperumbudur, B., von Luxburg, U.
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)
ei
von Luxburg, U., Franz, V.
A Geometric Approach to Confidence Sets for Ratios: Fieller’s Theorem, Generalizations, and Bootstrap
Statistica Sinica, 19(3):1095-1117, July 2009 (article)
ei
Maier, M., von Luxburg, U., Hein, M.
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)
ei
Maier, M., Hein, M., von Luxburg, U.
Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters
Theoretical Computer Science, 410(19):1749-1764, April 2009 (article)
ei
Bubeck, S., von Luxburg, U.
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)
ei
von Luxburg, U., Bubeck, S., Jegelka, S., Kaufmann, M.
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)
ei
Ben-David, S., von Luxburg, U.
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)
ei
von Luxburg, U., Belkin, M., Bousquet, O.
Consistency of Spectral Clustering
Annals of Statistics, 36(2):555-586, April 2008 (article)
ei
von Luxburg, U.
A Tutorial on Spectral Clustering
Statistics and Computing, 17(4):395-416, December 2007 (article)
ei
Maier, M., Hein, M., von Luxburg, U.
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)
ei
Hein, M., Audibert, J., von Luxburg, U.
Graph Laplacians and their Convergence on Random Neighborhood Graphs
Journal of Machine Learning Research, 8, pages: 1325-1370, June 2007 (article)
ei
Maier, M., Hein, M., von Luxburg, U.
Cluster Identification in Nearest-Neighbor Graphs
(163), Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany, May 2007 (techreport)
ei
Ben-David, S., von Luxburg, U., Pal, D.
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)
ei
von Luxburg, U.
A tutorial on spectral clustering
(149), Max Planck Institute for Biological Cybernetics, Tübingen, August 2006 (techreport)
ei
von Luxburg, U., Bousquet, O., Belkin, M.
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)
ei
Hein, M., Audibert, J., von Luxburg, U.
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)
ei
von Luxburg, U., Ben-David, S.
Towards a Statistical Theory of Clustering. Presented at the PASCAL workshop on clustering, London
Presented at the PASCAL workshop on clustering, London, 2005 (techreport)
ei
Bousquet, O., von Luxburg, U., Rätsch, G.
Advanced Lectures on Machine Learning
ML Summer Schools 2003, LNAI 3176, pages: 240, Springer, Berlin, Germany, ML Summer Schools, September 2004 (proceedings)
ei
von Luxburg, U., Bousquet, O.
Distance-Based Classification with Lipschitz Functions
Journal of Machine Learning Research, 5, pages: 669-695, June 2004 (article)
ei
von Luxburg, U., Bousquet, O., Schölkopf, B.
A Compression Approach to Support Vector Model Selection
Journal of Machine Learning Research, 5, pages: 293-323, April 2004 (article)
ei
von Luxburg, U.
Statistical Learning with Similarity and Dissimilarity Functions
pages: 1-166, Technische Universität Berlin, Germany, Technische Universität Berlin, Germany, 2004 (phdthesis)
ei
von Luxburg, U., Bousquet, O., Belkin, M.
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)