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ORCIDUlrike von Luxburg
Person information
- affiliation: University of Tübingen, Germany
- affiliation (former): University of Hamburg, Germany
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2020 – today
- 2024
[j16]David-Elias Künstle
, Ulrike von Luxburg:
cblearn: Comparison-based Machine Learning in Python. J. Open Source Softw. 9(98): 6139 (2024)- [i41]Sebastian Bordt, Ulrike von Luxburg:
Statistics without Interpretation: A Sober Look at Explainable Machine Learning. CoRR abs/2402.02870 (2024) - [i40]Robi Bhattacharjee, Ulrike von Luxburg:
Auditing Local Explanations is Hard. CoRR abs/2407.13281 (2024) - [i39]Sebastian Bordt, Suraj Srinivas, Valentyn Boreiko, Ulrike von Luxburg:
How much can we forget about Data Contamination? CoRR abs/2410.03249 (2024) - [i38]Gunnar König, Eric Günther, Ulrike von Luxburg:
Disentangling Interactions and Dependencies in Feature Attribution. CoRR abs/2410.23772 (2024) - 2023
- [j15]Solveig Klepper, Christian Elbracht, Diego Fioravanti, Jakob Kneip, Luca Rendsburg, Maximilian Teegen, Ulrike von Luxburg:
Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees. J. Mach. Learn. Res. 24: 190:1-190:56 (2023) - [j14]Leena Chennuru Vankadara, Michael Lohaus, Siavash Haghiri, Faiz Ul Wahab, Ulrike von Luxburg:
Insights into Ordinal Embedding Algorithms: A Systematic Evaluation. J. Mach. Learn. Res. 24: 191:1-191:83 (2023) - [j13]Solveig Klepper, Ulrike von Luxburg:
Relating graph auto-encoders to linear models. Trans. Mach. Learn. Res. 2023 (2023) - [c45]Sebastian Bordt, Ulrike von Luxburg:
From Shapley Values to Generalized Additive Models and back. AISTATS 2023: 709-745 - [c44]Sebastian Bordt, Uddeshya Upadhyay, Zeynep Akata, Ulrike von Luxburg:
The Manifold Hypothesis for Gradient-Based Explanations. CVPR Workshops 2023: 3697-3702 - [c43]Moritz Haas, David Holzmüller, Ulrike von Luxburg, Ingo Steinwart:
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension. NeurIPS 2023 - [i37]Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery:
AI for Science: An Emerging Agenda. CoRR abs/2303.04217 (2023) - [i36]Sebastian Bordt, Ulrike von Luxburg:
ChatGPT Participates in a Computer Science Exam. CoRR abs/2303.09461 (2023) - [i35]Moritz Haas, David Holzmüller, Ulrike von Luxburg, Ingo Steinwart:
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension. CoRR abs/2305.14077 (2023) - 2022
- [c42]Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg:
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. AISTATS 2022: 1503-1526 - [c41]Sebastian Bordt, Ulrike von Luxburg:
A Bandit Model for Human-Machine Decision Making with Private Information and Opacity. AISTATS 2022: 7300-7319 - [c40]Sebastian Bordt, Michèle Finck, Eric Raidl, Ulrike von Luxburg:
Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts. FAccT 2022: 891-905 - [c39]Leena Chennuru Vankadara, Luca Rendsburg, Ulrike von Luxburg, Debarghya Ghoshdastidar:
Interpolation and Regularization for Causal Learning. NeurIPS 2022 - [i34]Sebastian Bordt, Michèle Finck, Eric Raidl, Ulrike von Luxburg:
Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts. CoRR abs/2201.10295 (2022) - [i33]Leena Chennuru Vankadara, Luca Rendsburg, Ulrike von Luxburg, Debarghya Ghoshdastidar:
Interpolation and Regularization for Causal Learning. CoRR abs/2202.09054 (2022) - [i32]Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg:
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. CoRR abs/2203.03353 (2022) - [i31]Sebastian Bordt, Uddeshya Upadhyay, Zeynep Akata, Ulrike von Luxburg:
The Manifold Hypothesis for Gradient-Based Explanations. CoRR abs/2206.07387 (2022) - [i30]Sebastian Bordt, Ulrike von Luxburg:
From Shapley Values to Generalized Additive Models and back. CoRR abs/2209.04012 (2022) - [i29]Solveig Klepper, Ulrike von Luxburg:
Relating graph auto-encoders to linear models. CoRR abs/2211.01858 (2022) - [i28]Luca Rendsburg, Leena Chennuru Vankadara, Debarghya Ghoshdastidar, Ulrike von Luxburg:
A Consistent Estimator for Confounding Strength. CoRR abs/2211.01903 (2022) - [i27]Moritz Haas, Bedartha Goswami, Ulrike von Luxburg:
Pitfalls of Climate Network Construction: A Statistical Perspective. CoRR abs/2211.02888 (2022) - [i26]Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery:
Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382). Dagstuhl Reports 12(9): 150-199 (2022) - 2021
- [c38]Leena C. Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar:
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models. AISTATS 2021: 3817-3825 - [i25]Sascha Meyen, Frieder Göppert, Helen Alber, Ulrike von Luxburg, Volker H. Franz:
Specialists Outperform Generalists in Ensemble Classification. CoRR abs/2107.04381 (2021) - [i24]Leena C. Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar:
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models. CoRR abs/2110.09476 (2021) - 2020
- [c37]Damien Garreau, Ulrike von Luxburg:
Explaining the Explainer: A First Theoretical Analysis of LIME. AISTATS 2020: 1287-1296 - [c36]Michael Lohaus, Michaël Perrot, Ulrike von Luxburg:
Too Relaxed to Be Fair. ICML 2020: 6360-6369 - [c35]Luca Rendsburg, Holger Heidrich, Ulrike von Luxburg:
NetGAN without GAN: From Random Walks to Low-Rank Approximations. ICML 2020: 8073-8082 - [i23]Damien Garreau, Ulrike von Luxburg:
Explaining the Explainer: A First Theoretical Analysis of LIME. CoRR abs/2001.03447 (2020) - [i22]Christian Elbracht, Diego Fioravanti, Solveig Klepper, Jakob Kneip, Luca Rendsburg, Maximilian Teegen, Ulrike von Luxburg:
Tangles: From Weak to Strong Clustering. CoRR abs/2006.14444 (2020) - [i21]Sebastian Bordt, Ulrike von Luxburg:
When Humans and Machines Make Joint Decisions: A Non-Symmetric Bandit Model. CoRR abs/2007.04800 (2020) - [i20]Damien Garreau, Ulrike von Luxburg:
Looking deeper into LIME. CoRR abs/2008.11092 (2020)
2010 – 2019
- 2019
- [c34]Michaël Perrot, Ulrike von Luxburg:
Boosting for Comparison-Based Learning. IJCAI 2019: 1844-1850 - [c33]Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg:
Foundations of Comparison-Based Hierarchical Clustering. NeurIPS 2019: 7454-7464 - [i19]Siavash Haghiri, Felix A. Wichmann, Ulrike von Luxburg:
Comparison-Based Framework for Psychophysics: Lab versus Crowdsourcing. CoRR abs/1905.07234 (2019) - [i18]Michael Lohaus, Philipp Hennig, Ulrike von Luxburg:
Uncertainty Estimates for Ordinal Embeddings. CoRR abs/1906.11655 (2019) - [i17]Siavash Haghiri, Felix A. Wichmann, Ulrike von Luxburg:
Estimation of perceptual scales using ordinal embedding. CoRR abs/1908.07962 (2019) - [i16]Siavash Haghiri, Leena Chennuru Vankadara, Ulrike von Luxburg:
Large scale representation learning from triplet comparisons. CoRR abs/1912.01666 (2019) - 2018
- [j12]Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike von Luxburg:
Design and Analysis of the NIPS 2016 Review Process. J. Mach. Learn. Res. 19: 49:1-49:34 (2018) - [c32]Siavash Haghiri, Damien Garreau, Ulrike von Luxburg:
Comparison-Based Random Forests. ICML 2018: 1866-1875 - [c31]Cheng Tang, Damien Garreau, Ulrike von Luxburg:
When do random forests fail? NeurIPS 2018: 2987-2997 - [c30]Debarghya Ghoshdastidar, Ulrike von Luxburg:
Practical Methods for Graph Two-Sample Testing. NeurIPS 2018: 3019-3028 - [c29]Leena Chennuru Vankadara, Ulrike von Luxburg:
Measures of distortion for machine learning. NeurIPS 2018: 4891-4900 - [i15]Siavash Haghiri, Damien Garreau, Ulrike von Luxburg:
Comparison-Based Random Forests. CoRR abs/1806.06616 (2018) - [i14]Michaël Perrot, Ulrike von Luxburg:
Boosting for Comparison-Based Learning. CoRR abs/1810.13333 (2018) - [i13]Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg:
Foundations of Comparison-Based Hierarchical Clustering. CoRR abs/1811.00928 (2018) - [i12]Debarghya Ghoshdastidar, Ulrike von Luxburg:
Practical methods for graph two-sample testing. CoRR abs/1811.12752 (2018) - 2017
- [j11]Matthäus Kleindessner, Ulrike von Luxburg:
Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis. J. Mach. Learn. Res. 18: 58:1-58:52 (2017) - [c28]Siavash Haghiri, Debarghya Ghoshdastidar, Ulrike von Luxburg:
Comparison-Based Nearest Neighbor Search. AISTATS 2017: 851-859 - [c27]Debarghya Ghoshdastidar, Maurilio Gutzeit, Alexandra Carpentier, Ulrike von Luxburg:
Two-Sample Tests for Large Random Graphs Using Network Statistics. COLT 2017: 954-977 - [c26]Matthäus Kleindessner, Ulrike von Luxburg:
Kernel functions based on triplet comparisons. NIPS 2017: 6807-6817 - [e4]Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, Roman Garnett:
Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA. 2017 [contents] - [i11]Siavash Haghiri, Debarghya Ghoshdastidar, Ulrike von Luxburg:
Comparison Based Nearest Neighbor Search. CoRR abs/1704.01460 (2017) - [i10]Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike von Luxburg:
Design and Analysis of the NIPS 2016 Review Process. CoRR abs/1708.09794 (2017) - 2016
- [j10]Tobias Lang, Florian Flachsenberg, Ulrike von Luxburg, Matthias Rarey:
Feasibility of Active Machine Learning for Multiclass Compound Classification. J. Chem. Inf. Model. 56(1): 12-20 (2016) - [c25]Mehdi S. M. Sajjadi, Morteza Alamgir, Ulrike von Luxburg:
Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines. L@S 2016: 369-378 - [e3]Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, Roman Garnett:
Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain. 2016 [contents] - [i9]Matthäus Kleindessner, Ulrike von Luxburg:
Lens depth function and k-relative neighborhood graph: versatile tools for ordinal data analysis. CoRR abs/1602.07194 (2016) - [i8]Matthäus Kleindessner, Ulrike von Luxburg:
Kernel functions based on triplet similarity comparisons. CoRR abs/1607.08456 (2016) - [i7]Maria-Florina Balcan, Shai Ben-David, Ruth Urner, Ulrike von Luxburg:
Foundations of Unsupervised Learning (Dagstuhl Seminar 16382). Dagstuhl Reports 6(9): 94-109 (2016) - 2015
- [c24]Matthäus Kleindessner, Ulrike von Luxburg:
Dimensionality estimation without distances. AISTATS 2015 - [i6]Mehdi S. M. Sajjadi, Morteza Alamgir, Ulrike von Luxburg:
Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines. CoRR abs/1506.00852 (2015) - 2014
- [j9]Ulrike von Luxburg, Agnes Radl, Matthias Hein:
Hitting and commute times in large random neighborhood graphs. J. Mach. Learn. Res. 15(1): 1751-1798 (2014) - [j8]Kamalika Chaudhuri, Sanjoy Dasgupta, Samory Kpotufe, Ulrike von Luxburg:
Consistent Procedures for Cluster Tree Estimation and Pruning. IEEE Trans. Inf. Theory 60(12): 7900-7912 (2014) - [c23]Matthäus Kleindessner, Ulrike von Luxburg:
Uniqueness of Ordinal Embedding. COLT 2014: 40-67 - [c22]Morteza Alamgir, Gábor Lugosi, Ulrike von Luxburg:
Density-preserving quantization with application to graph downsampling. COLT 2014: 543-559 - [c21]Yoshikazu Terada, Ulrike von Luxburg:
Local Ordinal Embedding. ICML 2014: 847-855 - [c20]Sven Kurras, Ulrike von Luxburg, Gilles Blanchard:
The f-Adjusted Graph Laplacian: a Diagonal Modification with a Geometric Interpretation. ICML 2014: 1530-1538 - 2013
- [c19]Ulrike von Luxburg, Morteza Alamgir:
Density estimation from unweighted k-nearest neighbor graphs: a roadmap. NIPS 2013: 225-233 - 2012
- [c18]Morteza Alamgir, Ulrike von Luxburg:
Shortest path distance in random k-nearest neighbor graphs. ICML 2012 - [c17]Ulrike von Luxburg, Robert C. Williamson, Isabelle Guyon:
Clustering: Science or Art? ICML Unsupervised and Transfer Learning 2012: 65-80 - 2011
- [c16]Samory Kpotufe, Ulrike von Luxburg:
Pruning nearest neighbor cluster trees. ICML 2011: 225-232 - [c15]Dario García-García, Ulrike von Luxburg, Raúl Santos-Rodríguez:
Risk-Based Generalizations of f-divergences. ICML 2011: 417-424 - [c14]Morteza Alamgir, Ulrike von Luxburg:
Phase transition in the family of p-resistances. NIPS 2011: 379-387 - [c13]Sham M. Kakade, Ulrike von Luxburg:
Preface. COLT 2011 - [e2]Sham M. Kakade, Ulrike von Luxburg:
COLT 2011 - The 24th Annual Conference on Learning Theory, June 9-11, 2011, Budapest, Hungary. JMLR Proceedings 19, JMLR.org 2011 [contents] - [r1]Ulrike von Luxburg, Bernhard Schölkopf:
Statistical Learning Theory: Models, Concepts, and Results. Inductive Logic 2011: 651-706 - [i5]Markus Maier, Ulrike von Luxburg, Matthias Hein:
How the result of graph clustering methods depends on the construction of the graph. CoRR abs/1102.2075 (2011) - [i4]Samory Kpotufe, Ulrike von Luxburg:
Pruning nearest neighbor cluster trees. CoRR abs/1105.0540 (2011) - 2010
- [c12]Morteza Alamgir, Ulrike von Luxburg:
Multi-agent Random Walks for Local Clustering on Graphs. ICDM 2010: 18-27 - [c11]Ulrike von Luxburg, Agnes Radl, Matthias Hein:
Getting lost in space: Large sample analysis of the resistance distance. NIPS 2010: 2622-2630 - [i3]Ulrike von Luxburg, Agnes Radl, Matthias Hein:
Hitting times, commute distances and the spectral gap for large random geometric graphs. CoRR abs/1003.1266 (2010)
2000 – 2009
- 2009
- [j7]Ulrike von Luxburg:
Clustering Stability: An Overview. Found. Trends Mach. Learn. 2(3): 235-274 (2009) - [j6]Sébastien Bubeck, Ulrike von Luxburg:
Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions. J. Mach. Learn. Res. 10: 657-698 (2009) - [j5]Markus Maier, Matthias Hein, Ulrike von Luxburg:
Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters. Theor. Comput. Sci. 410(19): 1749-1764 (2009) - [c10]Stefanie Jegelka, Arthur Gretton, Bernhard Schölkopf, Bharath K. Sriperumbudur, Ulrike von Luxburg:
Generalized Clustering via Kernel Embeddings. KI 2009: 144-152 - 2008
- [c9]Shai Ben-David, Ulrike von Luxburg:
Relating Clustering Stability to Properties of Cluster Boundaries. COLT 2008: 379-390 - [c8]Markus Maier, Ulrike von Luxburg, Matthias Hein:
Influence of graph construction on graph-based clustering measures. NIPS 2008: 1025-1032 - 2007
- [j4]Matthias Hein, Jean-Yves Audibert, Ulrike von Luxburg:
Graph Laplacians and their Convergence on Random Neighborhood Graphs. J. Mach. Learn. Res. 8: 1325-1368 (2007) - [j3]Ulrike von Luxburg:
A tutorial on spectral clustering. Stat. Comput. 17(4): 395-416 (2007) - [c7]Markus Maier, Matthias Hein, Ulrike von Luxburg:
Cluster Identification in Nearest-Neighbor Graphs. ALT 2007: 196-210 - [c6]Ulrike von Luxburg, Sébastien Bubeck, Stefanie Jegelka, Michael Kaufmann:
Consistent Minimization of Clustering Objective Functions. NIPS 2007: 961-968 - [i2]Ulrike von Luxburg:
A Tutorial on Spectral Clustering. CoRR abs/0711.0189 (2007) - 2006
- [c5]Shai Ben-David, Ulrike von Luxburg, Dávid Pál:
A Sober Look at Clustering Stability. COLT 2006: 5-19 - [i1]Matthias Hein, Jean-Yves Audibert, Ulrike von Luxburg:
Graph Laplacians and their convergence on random neighborhood graphs. CoRR abs/math/0608522 (2006) - 2005
- [c4]Matthias Hein, Jean-Yves Audibert, Ulrike von Luxburg:
From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians. COLT 2005: 470-485 - 2004
- [b1]Ulrike von Luxburg:
Statistical learning with similarity and dissimilarity functions. Technical University of Berlin, Germany, Logos-Verlag 2004, ISBN 3-8325-0767-1, pp. 1-166 - [j2]Ulrike von Luxburg, Olivier Bousquet, Bernhard Schölkopf:
A Compression Approach to Support Vector Model Selection. J. Mach. Learn. Res. 5: 293-323 (2004) - [j1]Ulrike von Luxburg, Olivier Bousquet:
Distance-Based Classification with Lipschitz Functions. J. Mach. Learn. Res. 5: 669-695 (2004) - [c3]Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin:
On the Convergence of Spectral Clustering on Random Samples: The Normalized Case. COLT 2004: 457-471 - [c2]Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin:
Limits of Spectral Clustering. NIPS 2004: 857-864 - [e1]Olivier Bousquet, Ulrike von Luxburg, Gunnar Rätsch:
Advanced Lectures on Machine Learning, ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures. Lecture Notes in Computer Science 3176, Springer 2004, ISBN 3-540-23122-6 [contents] - 2003
- [c1]Ulrike von Luxburg, Olivier Bousquet:
Distance-Based Classification with Lipschitz Functions. COLT 2003: 314-328
Coauthor Index
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