M. Alamgir and U. von Luxburg: Multi-agent random walks for local
clustering. International Conference on Data Minig (ICDM), 2010.
pdf
U. von Luxburg and A. Radl and M. Hein: Getting lost in space: Large
sample analysis of the commute distance. Neural Information Processing
Systems (NIPS), 2010.
pdf (paper with supplement)
An annoying typo
U. von Luxburg. Clustering stability: an overview.
Foundations and Trends in Machine Learning 2 (3), 235-274, 2010.
pdf
I. Guyon, U. von Luxburg, R. Williamson: Clustering: Science or
Art? Opinion paper for the NIPS workshop "Clustering: Science or Art",
2009.
pdf
U. von Luxburg. Evidenzkriterien in der Informatik (in
German). In:
E. Engelen and C. Fleischhack and G. Galizia and K. Landfester (Eds):
Heureka: Evidenzkriterien in den Wissenschaften. Springer, Berlin,
2010.
pdf
U. von Luxburg and B. Schölkopf. Statistical Learning Theory:
Models, Concepts, and Results. In:
D. Gabbay, S. Hartmann and J. Woods (Eds).
Handbook of the History of Logic,
vol 10: Inductive Logic. In Press.
pdf
S. Bubeck, M. Meila, U. von Luxburg. How the initialization affects the stability of the
k-means algorithm. Draft, 2009.
link
This article has been written for a book that compares how different
scientific disciplines establish their results and what kind of
evidence one has to give before a result is considered to be true.
link
to the book
This is a high level overview paper on statistical learning theory. We
try to convey the major ideas and insights of SLT, rather than to dive
into technical details. It is intended for a broader audience - we do
not assume that the reader is particularly familiar with machine
learning.
S. Bubeck and U. von Luxburg. Nearest Neighbor Clustering: A
Baseline Method for Consistent Clustering with Arbitrary Objective
Functions. JMLR 10, 657-698, 2009. pdf
(Note: a previous version of this
manuscript was called "Overfitting of clustering and how to avoid
it").
M. Maier, U. von Luxburg, M. Hein: Influence of graph construction
on graph-based clustering measures. In: D. Koller and D. Schuurmans
and Y. Bengio and L. Bottou (Eds.): Advances in Neural Information
Processing Systems (NIPS) 22, 2009. paper (pdf)
and
supplement (pdf)
(For this paper
Markus Maier received the NIPS best student paper award. ).
U. von Luxburg, S. Bubeck, S. Jegelka, M. Kaufmann: Consistent Minimization of Clustering Objective
Functions. In: J. Platt and D. Koller and Y. Singer and S. Roweis
(editors): Advances in Neural Information Processing Systems (NIPS) 21,
MIT Press, Cambridge, MA. 2008
paper (pdf), and
supplement (pdf).
S. Ben-David and U. von Luxburg: Relating clustering stability to properties of cluster boundaries. In: R. Servedio and T. Zhang (Eds.): Proceedings of the 21st Annual Conference on Learning Theory (COLT), pp. 379-390. Springer, Berlin, 2008. pdf
M. Maier and M. Hein and U. von Luxburg. Optimal construction of k-nearest neighbor graphs for identifying noisy clusters. Theoretical Computer Science 410, p. 1749-1764, 2009. preprint as pdf
M. Maier, M. Hein, U. von Luxburg. Cluster identification in
nearest neighbor graphs. In: Marcus Hutter and Rocco A. Servedio and Eiji
Takimoto (Eds): Algorithmic Learning Theory (ALT) 18,
pp. 196--210. Springer, 2007. pdf
(conference paper),
pdf
(technical report with proof details)
(For this paper
Markus Maier received the ALT best student paper award. ).
U. von Luxburg and V. Franz. A Geometric Approach to Confidence Sets for Ratios:
Fieller's Theorem, Generalizations, and Bootstrap. Statistica Sinica
19 (3), pp. 1095 - 1117, 2009
preprint of the paper (pdf) and
supplement (pdf)
[A preliminary version of this paper with less results appeared as:
Confidence sets for ratios: A purely
geometric approach to Fieller's theorem. Technical Report 133, Max Planck Institute
for Biological Cybernetics, 2004. pdf]
U. von Luxburg. A Tutorial on Spectral Clustering. Statistics and
Computing 17(4): 395-416, 2007.
paper
(pdf) and
some typos (txt).
[A
previous version of this paper appeared as Technical Report 149,
Max Planck Institute for Biological Cybernetics, 2006. ] There also
exists a
video lecture
where I give a tutorial on spectral clustering (and
other clustering algorithms). And here is a nice
matlab demo which can be used to play with spectral clustering (written by Matthias Hein and
me).
S. Ben-David, U.von Luxburg, D. Pal: A Sober Look on Clustering Stability.
In: G. Lugosi and H. Simon, editors, Proceedings of the 19th Annual Conference
on Learning Theory (COLT), pages 5 - 19, Springer, 2006.
pdf
(For this paper
David Pal received a COLT best student paper award).
Matthias Hein, Jean-Yves Audibert, Ulrike von Luxburg. Graph Laplacians and their Convergence on Random Neighborhood Graphs. JMLR 8:1325--1370, 2007. pdf,
M. Hein, J.-Y. Audibert, and U. von Luxburg. From Graphs to Manifolds
- Weak and Strong Pointwise Consistency of Graph Laplacians. In: P. Auer and
Ron Meir, editors, Proceedings of the 18th Annual Conferecnce on Learning
Theory (COLT), pages 470-485. Springer, 2005. pdf
(For this paper Matthias
Hein received a COLT best student paper award).
U. von Luxburg, S. Ben-David. Towards a statistical theory for clustering.
Presented at the PASCAL Workshop on Statistics and Optimization of Clustering
Workshop 4-5 July 2005, London,
U.K. pdf.
[This opinion paper suggested some kind of "research program". Note that in the
meantime, some of the questions have already been solved, while some
of the other questions turned out to be not so useful after all...]
U. von Luxburg, M. Belkin, and O. Bousquet. Consistency of
spectral clustering.
Annals of Statistics, 36 (2), 555-586, 2008 pdf
[Previous version appeared as Technical Report 134, Max Planck Institute for Biological
Cybernetics. ]
U. von Luxburg, O. Bousquet, and M. Belkin. Limits of spectral
clustering. In Lawrence K. Saul, Yair Weiss, and Leon Bottou, editors,
Advances in Neural Information Processing Systems (NIPS) 17. MIT Press,
Cambridge, MA, 2005. pdf
(For this paper I received the NIPS outstanding student
paper award).
U. von Luxburg, O. Bousquet, and M. Belkin. On the convergence of spectral clustering on random samples: the normalized case. In J. Shawe-Taylor and Y. Singer, editors, Proceedings of the 17th Annual Conference on Learning Theory (COLT), pages 457-471. Springer, 2004. pdf
U. von Luxburg and O. Bousquet. Distance-based classification with Lipschitz functions. Journal for Machine Learning Research, 5:669-695, 2004. pdf
U.von Luxburg and O.Bousquet. Distance-based classification with
Lipschitz functions. In B.Schölkopf and M.K. Warmuth, editors, Proceedings
of the 16th Annual Conference on Learning Theory (COLT), pages 314-328.
Springer, 2003. pdf
(For this paper I received the COLT Mark Fulk best student
paper award).
U. von Luxburg, O.Bousquet, and B.Schölkopf. A compression
approach to support vector model selection. Journal for Machine Learning
Research, 5:293-323, 2004.
pdf
[An earlier version of this paper appeared as Technical Report 101, Max Planck Institute for Biological Cybernetics].
O. Bousquet, U. von Luxburg, and G.Rätsch, editors. Advanced Lectures on Machine Learning, volume 3176 of Springer Lecture Notes in Artificial Intelligence. Springer Verlag, Heidelberg, 2004. link
U. von Luxburg. Statistical Learning with Similarity and Dissimilarity Functions. PhD thesis, Technical University of Berlin, 2004. pdf