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2018


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When do random forests fail?

Tang, C., Garreau, D., von Luxburg, U.

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

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Project Page [BibTex]

2018


Project Page [BibTex]


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Comparison-Based Random Forests

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

In ICML, 35th International Conference on Machine Learning, July 2018 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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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, 2018, *equal contribution (article) Accepted

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arXiv [BibTex]

arXiv [BibTex]


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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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[BibTex]

[BibTex]


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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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[BibTex]

[BibTex]

2017


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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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[BibTex]

2017


[BibTex]


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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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[BibTex]

[BibTex]


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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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[BibTex]

[BibTex]


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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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[BibTex]

[BibTex]