Ulrike von Luxburg

2018

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)

arXiv link (url) Project Page [BibTex]

Comparison-Based Random Forests

Haghiri, S., Garreau, D., Luxburg, U. V.

International Conference on Machine learning (ICML), 2018 (conference)

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link (url) [BibTex]

slt Haghiri, S., Garreau, D., Luxburg, U. V. Comparison-Based Random Forests International Conference on Machine learning (ICML), 2018 (conference)

link (url) [BibTex]

Design and Analysis of the NIPS 2016 Review Process

Shah*, N., Tabibian*, B., Muandet, K., Guyon, I., von Luxburg, U.

Journal of Machine Learning Research, 19(49):1-34, 2018, *equal contribution (article)

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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)

arXiv link (url) Project Page [BibTex]

Foundations of Comparison-Based Hierarchical Clustering

Ghoshdastidar, D., Perrot, M., von Luxburg, U.

2018, arXiv preprint (arXiv:1811.00928) (article)

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slt Ghoshdastidar, D., Perrot, M., von Luxburg, U. Foundations of Comparison-Based Hierarchical Clustering 2018, arXiv preprint (arXiv:1811.00928) (article)

Measures of distortion for machine learning

Vankadara, L., von Luxburg, U.

In Proceedings Neural Information Processing Systems, Neural Information Processing Systems (NIPS 2018) , 2018 (inproceedings)

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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)

Practical Methods for Graph Two-Sample Testing

Ghoshdastidar, D., von Luxburg, U.

In Proceedings Neural Information Processing Systems, Neural Information Processing Systems (NIPS 2018) , 2018 (inproceedings)

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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)

Perrot, M., von Luxburg, U.

Boosting for Comparison-Based Learning

2018, arXiv preprint (arXiv:1810.13333) (article)

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slt Perrot, M., von Luxburg, U. Boosting for Comparison-Based Learning 2018, arXiv preprint (arXiv:1810.13333) (article)

2017

Comparison-based nearest neighbor search

Haghiri, S., Ghoshdastidar, D., von Luxburg, U.

In Artificial Intelligence and Statistics, Artificial Intelligence and Statistics (AISTATS), 2017 (inproceedings)

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2017

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)

Kernel functions based on triplet comparisons

Kleindessner, M., von Luxburg, U.

In Proceedings Neural Information Processing Systems, Neural Information Processing Systems (NIPS), 2017 (inproceedings)

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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)

Two-sample Hypothesis Testing for Inhomogeneous Random Graphs

Ghoshdastidar, D., Gutzeit, M., Carpentier, A., von Luxburg, U.

2017, arXiv preprint (arXiv:1707.00833) (article)

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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)

Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis

Kleindessner, M., von Luxburg, U.

Journal of Machine Learning Research (JMLR), Journal of Machine Learning Research, 18, 2017 (article)

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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)

Two-sample tests for large random graphs using network statistics

Ghoshdastidar, D., Gutzeit, M., Carpentier, A., von Luxburg, U.

In Conference on Computational Learning Theory (COLT), Conference on Computational Learning Theory (COLT), 2017 (inproceedings)

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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)

Sajjadi, M. S. M., Alamgir, M., von Luxburg, U.

2016

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)

Arxiv Peer-Grading dataset request [BibTex]

2016

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)

Arxiv Peer-Grading dataset request [BibTex]

2015

Peer grading in a course on algorithms and data structures

Sajjadi, M. S. M., Alamgir, M., von Luxburg, U.

Workshop on Machine Learning for Education (ML4Ed) at the 32th International Conference on Machine Learning (ICML), 2015 (conference)

2015

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)

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 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)

von Luxburg, U., Alamgir, M.

2013

How the result of graph clustering methods depends on the construction of the graph

Maier, M., von Luxburg, U., Hein, M.

ESAIM: Probability & Statistics, 17, pages: 370-418, January 2013 (article)

Abstract

We study the scenario of graph-based clustering algorithms such as spectral clustering. Given a set of data points, one rst has to construct a graph on the data points and then apply a graph clustering algorithm to nd a suitable partition of the graph. Our main question is if and how the construction of the graph (choice of the graph, choice of parameters, choice of weights) in uences the outcome of the nal clustering result. To this end we study the convergence of cluster quality measures such as the normalized cut or the Cheeger cut on various kinds of random geometric graphs as the sample size tends to innity. It turns out that the limit values of the same objective function are systematically dierent on dierent types of graphs. This implies that clustering results systematically depend on the graph and can be very dierent for dierent types of graph. We provide examples to illustrate the implications on spectral clustering.

PDF DOI [BibTex]

2013

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)

PDF DOI [BibTex]

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 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)

2012

How the initialization affects the stability of the k-means algorithm

Bubeck, S., Meila, M., von Luxburg, U.

ESAIM: Probability and Statistics, 16, pages: 436-452, January 2012 (article)

Web DOI [BibTex]

2012

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)

Web DOI [BibTex]

Clustering: Science or Art?

von Luxburg, U., Williamson, R., Guyon, I.

In JMLR Workshop and Conference Proceedings, Volume 27, pages: 65-79, Workshop on Unsupervised Learning and Transfer Learning, 2012 (inproceedings)

Abstract

We examine whether the quality of dierent clustering algorithms can be compared by a general, scientically sound procedure which is independent of particular clustering algorithms. We argue that the major obstacle is the diculty in evaluating a clustering algorithm without taking into account the context: why does the user cluster his data in the rst place, and what does he want to do with the clustering afterwards? We argue that clustering should not be treated as an application-independent mathematical problem, but should always be studied in the context of its end-use. Dierent techniques to evaluate clustering algorithms have to be developed for dierent uses of clustering. To simplify this procedure we argue that it will be useful to build a \taxonomy of clustering problems" to identify clustering applications which can be treated in a unied way and that such an eort will be more fruitful than attempting the impossible | developing \optimal" domain-independent clustering algorithms or even classifying clustering algorithms in terms of how they work.

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)