Ulrike von Luxburg

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 60

Top Venues

CoRR

AISTATS

NeurIPS

J. Mach. Learn. Res.

Technical University of Berlin, Germany (PHD)

Education History

Technical University of Berlin, Germany

PHD

Publications

Sebastian Bordt, Ulrike von Luxburg
Statistics without Interpretation: A Sober Look at Explainable Machine Learning. CoRR (2024)
Robi Bhattacharjee, Ulrike von Luxburg
Auditing Local Explanations is Hard. CoRR (2024)
Sebastian Bordt, Uddeshya Upadhyay, Zeynep Akata, Ulrike von Luxburg
The Manifold Hypothesis for Gradient-Based Explanations. CVPR Workshops (2023)
Sebastian Bordt, Ulrike von Luxburg
From Shapley Values to Generalized Additive Models and back. AISTATS (2023)
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 (2023)
Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery
AI for Science: An Emerging Agenda. CoRR (2023)
Solveig Klepper, Ulrike von Luxburg
Relating graph auto-encoders to linear models. Trans. Mach. Learn. Res. 2023 (2023)
Moritz Haas, David Holzmüller, Ulrike von Luxburg, Ingo Steinwart
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension. CoRR (2023)
Moritz Haas, David Holzmüller, Ulrike von Luxburg, Ingo Steinwart
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension. NeurIPS (2023)
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 (2023)
Sebastian Bordt, Ulrike von Luxburg
ChatGPT Participates in a Computer Science Exam. CoRR (2023)
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) (2022)
Sebastian Bordt, Ulrike von Luxburg
A Bandit Model for Human-Machine Decision Making with Private Information and Opacity. AISTATS (2022)
Sebastian Bordt, Ulrike von Luxburg
From Shapley Values to Generalized Additive Models and back. CoRR (2022)
Luca Rendsburg, Leena Chennuru Vankadara, Debarghya Ghoshdastidar, Ulrike von Luxburg
A Consistent Estimator for Confounding Strength. CoRR (2022)
Sebastian Bordt, Uddeshya Upadhyay, Zeynep Akata, Ulrike von Luxburg
The Manifold Hypothesis for Gradient-Based Explanations. CoRR (2022)
Sebastian Bordt, Michèle Finck, Eric Raidl, Ulrike von Luxburg
Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts. CoRR (2022)
Leena Chennuru Vankadara, Luca Rendsburg, Ulrike von Luxburg, Debarghya Ghoshdastidar
Interpolation and Regularization for Causal Learning. NeurIPS (2022)
Solveig Klepper, Ulrike von Luxburg
Relating graph auto-encoders to linear models. CoRR (2022)
Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. AISTATS (2022)
Leena Chennuru Vankadara, Luca Rendsburg, Ulrike von Luxburg, Debarghya Ghoshdastidar
Interpolation and Regularization for Causal Learning. CoRR (2022)
Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. CoRR (2022)
Sebastian Bordt, Michèle Finck, Eric Raidl, Ulrike von Luxburg
Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts. FAccT (2022)
Moritz Haas, Bedartha Goswami, Ulrike von Luxburg
Pitfalls of Climate Network Construction: A Statistical Perspective. CoRR (2022)
Sascha Meyen, Frieder Göppert, Helen Alber, Ulrike von Luxburg, Volker H. Franz
Specialists Outperform Generalists in Ensemble Classification. CoRR (2021)
Leena Chennuru Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models. AISTATS (2021)
Leena Chennuru Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models. CoRR (2021)
Sebastian Bordt, Ulrike von Luxburg
When Humans and Machines Make Joint Decisions: A Non-Symmetric Bandit Model. CoRR (2020)
Damien Garreau, Ulrike von Luxburg
Looking deeper into LIME. CoRR (2020)
Damien Garreau, Ulrike von Luxburg
Explaining the Explainer: A First Theoretical Analysis of LIME. AISTATS (2020)
Michael Lohaus, Michaël Perrot, Ulrike von Luxburg
Too Relaxed to Be Fair. ICML (2020)
Damien Garreau, Ulrike von Luxburg
Explaining the Explainer: A First Theoretical Analysis of LIME. CoRR (2020)
Christian Elbracht, Diego Fioravanti, Solveig Klepper, Jakob Kneip, Luca Rendsburg, Maximilian Teegen, Ulrike von Luxburg
Tangles: From Weak to Strong Clustering. CoRR (2020)
Luca Rendsburg, Holger Heidrich, Ulrike von Luxburg
NetGAN without GAN: From Random Walks to Low-Rank Approximations. ICML (2020)
Siavash Haghiri, Felix A. Wichmann, Ulrike von Luxburg
Comparison-Based Framework for Psychophysics: Lab versus Crowdsourcing. CoRR (2019)
Siavash Haghiri, Leena Chennuru Vankadara, Ulrike von Luxburg
Large scale representation learning from triplet comparisons. CoRR (2019)
Michaël Perrot, Ulrike von Luxburg
Boosting for Comparison-Based Learning. IJCAI (2019)
Siavash Haghiri, Felix A. Wichmann, Ulrike von Luxburg
Estimation of perceptual scales using ordinal embedding. CoRR (2019)
Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg
Foundations of Comparison-Based Hierarchical Clustering. NeurIPS (2019)
Michael Lohaus, Philipp Hennig, Ulrike von Luxburg
Uncertainty Estimates for Ordinal Embeddings. CoRR (2019)
Debarghya Ghoshdastidar, Ulrike von Luxburg
Practical Methods for Graph Two-Sample Testing. NeurIPS (2018)
Siavash Haghiri, Damien Garreau, Ulrike von Luxburg
Comparison-Based Random Forests. CoRR (2018)
Siavash Haghiri, Damien Garreau, Ulrike von Luxburg
Comparison-Based Random Forests. ICML (2018)
Michaël Perrot, Ulrike von Luxburg
Boosting for Comparison-Based Learning. CoRR (2018)
Cheng Tang, Damien Garreau, Ulrike von Luxburg
When do random forests fail? NeurIPS (2018)
Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg
Foundations of Comparison-Based Hierarchical Clustering. CoRR (2018)
Leena Chennuru Vankadara, Ulrike von Luxburg
Measures of distortion for machine learning. NeurIPS (2018)
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 (2018)
Debarghya Ghoshdastidar, Ulrike von Luxburg
Practical methods for graph two-sample testing. CoRR (2018)
Debarghya Ghoshdastidar, Maurilio Gutzeit, Alexandra Carpentier, Ulrike von Luxburg
Two-Sample Tests for Large Random Graphs Using Network Statistics. COLT (2017)
Siavash Haghiri, Debarghya Ghoshdastidar, Ulrike von Luxburg
Comparison Based Nearest Neighbor Search. CoRR (2017)
Matthäus Kleindessner, Ulrike von Luxburg
Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis. J. Mach. Learn. Res. 18 (2017)
Matthäus Kleindessner, Ulrike von Luxburg
Kernel functions based on triplet comparisons. NIPS (2017)
Siavash Haghiri, Debarghya Ghoshdastidar, Ulrike von Luxburg
Comparison-Based Nearest Neighbor Search. AISTATS (2017)
Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike von Luxburg
Design and Analysis of the NIPS 2016 Review Process. CoRR (2017)
Matthäus Kleindessner, Ulrike von Luxburg
Lens depth function and k-relative neighborhood graph: versatile tools for ordinal data analysis. CoRR (2016)
Tobias Lang, Florian Flachsenberg, Ulrike von Luxburg, Matthias Rarey
Feasibility of Active Machine Learning for Multiclass Compound Classification. J. Chem. Inf. Model. 56 (1) (2016)
Maria-Florina Balcan, Shai Ben-David, Ruth Urner, Ulrike von Luxburg
Foundations of Unsupervised Learning (Dagstuhl Seminar 16382). Dagstuhl Reports 6 (9) (2016)
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)
Matthäus Kleindessner, Ulrike von Luxburg
Kernel functions based on triplet similarity comparisons. CoRR (2016)
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 (2015)
Matthäus Kleindessner, Ulrike von Luxburg
Dimensionality estimation without distances. AISTATS (2015)
Kamalika Chaudhuri, Sanjoy Dasgupta, Samory Kpotufe, Ulrike von Luxburg
Consistent Procedures for Cluster Tree Estimation and Pruning. IEEE Trans. Inf. Theory 60 (12) (2014)
Ulrike von Luxburg, Agnes Radl, Matthias Hein
Hitting and commute times in large random neighborhood graphs. J. Mach. Learn. Res. 15 (1) (2014)
Sven Kurras, Ulrike von Luxburg, Gilles Blanchard
The f-Adjusted Graph Laplacian: a Diagonal Modification with a Geometric Interpretation. ICML (2014)
Matthäus Kleindessner, Ulrike von Luxburg
Uniqueness of Ordinal Embedding. COLT (2014)
Yoshikazu Terada, Ulrike von Luxburg
Local Ordinal Embedding. ICML (2014)
Morteza Alamgir, Gábor Lugosi, Ulrike von Luxburg
Density-preserving quantization with application to graph downsampling. COLT (2014)
Ulrike von Luxburg, Morteza Alamgir
Density estimation from unweighted k-nearest neighbor graphs: a roadmap. NIPS (2013)
Ulrike von Luxburg, Robert C. Williamson, Isabelle Guyon
Clustering: Science or Art? ICML Unsupervised and Transfer Learning (2012)
Morteza Alamgir, Ulrike von Luxburg
Shortest path distance in random k-nearest neighbor graphs. ICML (2012)
Markus Maier, Ulrike von Luxburg, Matthias Hein
How the result of graph clustering methods depends on the construction of the graph CoRR (2011)
Sham M. Kakade, Ulrike von Luxburg
Preface. COLT (2011)
Dario García-García, Ulrike von Luxburg, Raúl Santos-Rodríguez
Risk-Based Generalizations of f-divergences. ICML (2011)
Morteza Alamgir, Ulrike von Luxburg
Phase transition in the family of p-resistances. NIPS (2011)
Samory Kpotufe, Ulrike von Luxburg
Pruning nearest neighbor cluster trees. ICML (2011)
Samory Kpotufe, Ulrike von Luxburg
Pruning nearest neighbor cluster trees CoRR (2011)
Ulrike von Luxburg, Agnes Radl, Matthias Hein
Getting lost in space: Large sample analysis of the resistance distance. NIPS (2010)
Ulrike von Luxburg, Agnes Radl, Matthias Hein
Hitting times, commute distances and the spectral gap for large random geometric graphs CoRR (2010)
Morteza Alamgir, Ulrike von Luxburg
Multi-agent Random Walks for Local Clustering on Graphs. ICDM (2010)
Markus Maier, Matthias Hein, Ulrike von Luxburg
Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters. Theor. Comput. Sci. 410 (19) (2009)
Stefanie Jegelka, Arthur Gretton, Bernhard Schölkopf, Bharath K. Sriperumbudur, Ulrike von Luxburg
Generalized Clustering via Kernel Embeddings. KI (2009)
Sébastien Bubeck, Ulrike von Luxburg
Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions. J. Mach. Learn. Res. 10 (2009)
Ulrike von Luxburg
Clustering Stability: An Overview. Found. Trends Mach. Learn. 2 (3) (2009)
Shai Ben-David, Ulrike von Luxburg
Relating Clustering Stability to Properties of Cluster Boundaries. COLT (2008)
Markus Maier, Ulrike von Luxburg, Matthias Hein
Influence of graph construction on graph-based clustering measures. NIPS (2008)
Ulrike von Luxburg, Sébastien Bubeck, Stefanie Jegelka, Michael Kaufmann
Consistent Minimization of Clustering Objective Functions. NIPS (2007)
Markus Maier, Matthias Hein, Ulrike von Luxburg
Cluster Identification in Nearest-Neighbor Graphs. ALT (2007)
Ulrike von Luxburg
A Tutorial on Spectral Clustering CoRR (2007)
Matthias Hein, Jean-Yves Audibert, Ulrike von Luxburg
Graph Laplacians and their Convergence on Random Neighborhood Graphs. J. Mach. Learn. Res. 8 (2007)
Ulrike von Luxburg
A tutorial on spectral clustering. Stat. Comput. 17 (4) (2007)
Matthias Hein, Jean-Yves Audibert, Ulrike von Luxburg
Graph Laplacians and their convergence on random neighborhood graphs CoRR (2006)
Shai Ben-David, Ulrike von Luxburg, Dávid Pál
A Sober Look at Clustering Stability. COLT (2006)
Matthias Hein, Jean-Yves Audibert, Ulrike von Luxburg
From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians. COLT (2005)
Ulrike von Luxburg, Olivier Bousquet, Bernhard Schölkopf
A Compression Approach to Support Vector Model Selection. J. Mach. Learn. Res. 5 (2004)
Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin
On the Convergence of Spectral Clustering on Random Samples: The Normalized Case. COLT (2004)
Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin
Limits of Spectral Clustering. NIPS (2004)
Ulrike von Luxburg, Olivier Bousquet
Distance-Based Classification with Lipschitz Functions. J. Mach. Learn. Res. 5 (2004)
Ulrike von Luxburg, Olivier Bousquet
Distance-Based Classification with Lipschitz Functions. COLT (2003)

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Publication Activity (10 Years)

Top Topics

Top Venues

Ulrike von Luxburg