PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

EPrints submitted by Ulrike Von Luxburg

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Number of EPrints submitted by this user: 25

On the Convergence of Spectral Clustering on Random Samples: The Normalized Case
Ulrike von Luxburg, Olivier Bousquet and Mikhail Belkin
In: COLT 2004, 1-4 July 2004, Banff, Canada.

A compression approach to support vector model selection
Ulrike Von Luxburg, Olivier Bousquet and Bernhard Schölkopf
JMLR Volume 5, pp. 293-323, 2004.

Distance-based classification with Lipschitz functions
Ulrike Von Luxburg and Olivier Bousquet
JMLR Volume 5, pp. 669-695, 2004.

A tutorial on spectral clustering
Ulrike v. Luxburg
Statistics and Computing Volume 17, Number 4, 2007.

Consistency of spectral clustering
Ulrike v. Luxburg, Mikhail Belkin and Olivier Bousquet
Annals of Statistics Volume ???, Number ???, 2005.

Consistent Minimization of Clustering Objective Functions
Ulrike v. Luxburg, Sebastien Bubeck, Stefanie Jegelka and Michael Kaufmann
In: NIPS 2007, Vancouver, Canada(2008).

Cluster identification in nearest neighbor graphs.
Markus Maier, Matthias Hein and Ulrike v. Luxburg
In: ALT 2007(2007).

A Sober Look on Clustering Stability
Shai Ben-David, David Pal and Ulrike v. Luxburg
In: COLT 2006(2006).

Limits of spectral clustering
Ulrike v. Luxburg, Olivier Bousquet and Mikhail Belkin
In: NIPS 2004(2005).

Nearest Neighbor Clustering: a baseline method for consistent cluteirng with arbitrary objective functions
Bubeck Sebastien and Ulrike v. Luxburg
JMLR Volume 10, pp. 657-698, 2009.

Optimal construction of k-nearest neibhbor graphs for identifying noisy clusters
Markus Maier, Matthias HEIN and Ulrike v. Luxburg
TCS Volume 410, Number 19, pp. 1749-1764, 2009.

Generalized clustering via kernel embeddings
Stefanie Jegelka, Arthur Gretton, Bernhard Schölkopf, Barath Sriperumbudur and Ulrike v. Luxburg
Generalized clustering via kernel embeddings Volume Proceedings of the 32nd Annual Conference on Artificial Intelligence, 2009.

A geometric approach to confidence sets for ratios: Fieller's theorem, generalizations and bootstrap
Ulrike v. Luxburg and Volker Franz
Statistica Sinica Volume 19, Number 3, pp. 1095-1117, 2009.

Influence of graph construction on graph-based clustering measures
Markus Maiker, Ulrike v. Luxburg and Matthias Hein
In: NIPS 2008, Vancouver, Canada(2009).

Clustering stability: an overview
Ulrike v. Luxburg
Foundations and Trends in Machine Learning 2009.

Clustering: Science or Art?
Isabelle Guyon, Robert Williamson and Ulrike v. Luxburg
In: NIPS Workshop: Clustering: Science or ARt?, Vancouver, Canada(2009).

Statistical Learning Theory: Models, Concepts and Results
Bernhard Schölkopf and Ulrike v. Luxburg
In: Handbook for the History of Logic (2009) ? .

Multi-agent random walks for local clustering
Morteza Alamgir and Ulrike v. Luxburg
In: ICDM(2010).

Shortest path distance in random k-nearest neighbor graphs. International Conference on Machine Learning
Morteza Alamgir and Ulrike v. Luxburg
ICML 2012.

Getting lost in space: Large sample analysis of the commute distance
Ulrike v. Luxburg, Agnes Radl and Matthias Hein
In: NIPS 2010(2010).

Pruning nearest neighbor cluster trees.
Ulrike v. Luxburg and Samory Kpotufe
In: ICML 2011(2011).

Risk-based generalizations of f-divergences
Dario Garcia-Garcia, Ulrike v. Luxburg and Raul Santos-Rodriguez
In: ICML 2011(2011).

Phase transition in the family of p-resistances.
Morteza Alamgir and Ulrike v. Luxburg
In: NIPS 2011(2011).

How the result of graph clustering methods depends on the construction of the graph
Markus Maier, Ulrike v. Luxburg and Matthias Hein
ESAIM Probablitiy and Statistics 2011.

How the initialization affects the stability of the k-means algorithm
Sebastien Bubeck, Marina Meila and Ulrike v. Luxburg
ESAIM: Probability and Statistics Volume 16, pp. 436-452, 2012.