| A tutorial on spectral clustering U Von Luxburg Statistics and computing 17 (4), 395-416, 2007 | 8105 | 2007 |
| Consistency of spectral clustering U Von Luxburg, M Belkin, O Bousquet The Annals of Statistics, 555-586, 2008 | 533 | 2008 |
| From graphs to manifolds–weak and strong pointwise consistency of graph Laplacians M Hein, JY Audibert, U Von Luxburg International Conference on Computational Learning Theory, 470-485, 2005 | 305 | 2005 |
| A sober look at clustering stability S Ben-David, U Von Luxburg, D Pál International Conference on Computational Learning Theory, 5-19, 2006 | 240 | 2006 |
| Graph Laplacians and their convergence on random neighborhood graphs M Hein, JY Audibert, U Luxburg Journal of Machine Learning Research 8 (Jun), 1325-1368, 2007 | 239 | 2007 |
| Clustering stability: an overview U Von Luxburg Now Publishers Inc, 2010 | 213 | 2010 |
| Influence of graph construction on graph-based clustering measures M Maier, UV Luxburg, M Hein Advances in neural information processing systems, 1025-1032, 2009 | 171 | 2009 |
| Regret bounds for the adaptive control of linear quadratic systems Y Abbasi-Yadkori, C Szepesvári Proceedings of the 24th Annual Conference on Learning Theory, 1-26, 2011 | 159 | 2011 |
| Clustering: Science or art? U Von Luxburg, RC Williamson, I Guyon Proceedings of ICML Workshop on Unsupervised and Transfer Learning, 65-79, 2012 | 145 | 2012 |
| Clustering: Science or art I Guyon, U Von Luxburg, RC Williamson NIPS 2009 workshop on clustering theory, 1-11, 2009 | 136 | 2009 |
| Limits of spectral clustering UV Luxburg, O Bousquet, M Belkin Advances in neural information processing systems, 857-864, 2005 | 136 | 2005 |
| Getting lost in space: Large sample analysis of the resistance distance UV Luxburg, A Radl, M Hein Advances in Neural Information Processing Systems, 2622-2630, 2010 | 129 | 2010 |
| Distance-based classification with Lipschitz functions U Luxburg, O Bousquet Journal of Machine Learning Research 5 (Jun), 669-695, 2004 | 128 | 2004 |
| Statistical learning theory: Models, concepts, and results U Von Luxburg, B Schölkopf Handbook of the History of Logic 10, 651-706, 2011 | 121 | 2011 |
| Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters M Maier, M Hein, U Von Luxburg Theoretical Computer Science 410 (19), 1749-1764, 2009 | 119 | 2009 |
| Towards a statistical theory of clustering U Von Luxburg, S Ben-David Pascal workshop on statistics and optimization of clustering, 20-26, 2005 | 96 | 2005 |
| Hitting and commute times in large random neighborhood graphs U Von Luxburg, A Radl, M Hein The Journal of Machine Learning Research 15 (1), 1751-1798, 2014 | 94 | 2014 |
| Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures O Bousquet, U von Luxburg, G Rätsch Springer, 2011 | 85 | 2011 |
| A compression approach to support vector model selection UV Luxburg, O Bousquet, B Schölkopf Journal of Machine Learning Research 5 (Apr), 293-323, 2004 | 71 | 2004 |
| Nearest neighbor clustering: A baseline method for consistent clustering with arbitrary objective functions S Bubeck, U Luxburg The Journal of Machine Learning Research 10, 657-698, 2009 | 66 | 2009 |
