On the Convergence of Spectral Clustering on Random Samples: The Normalized Case. - Ulrike von Luxburg; Olivier Bousquet; Mikhail Belkin (COLT, 2004)

Limits of Spectral Clustering. - Ulrike von Luxburg; Olivier Bousquet; Mikhail Belkin (NIPS, 2004)

Distance-Based Classification with Lipschitz Functions. - Ulrike von Luxburg; Olivier Bousquet (J. Mach. Learn. Res., 2004)

A Compression Approach to Support Vector Model Selection. - Ulrike von Luxburg; Olivier Bousquet; Bernhard Schölkopf (J. Mach. Learn. Res., 2004)

Statistical learning with similarity and dissimilarity functions. - Ulrike von Luxburg (Logos-Verlag, 2004)

From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians. - Matthias Hein 001; Jean-Yves Audibert; Ulrike von Luxburg (COLT, 2005)

A Sober Look at Clustering Stability. - Shai Ben-David; Ulrike von Luxburg; Dávid Pál (COLT, 2006)

Cluster Identification in Nearest-Neighbor Graphs. - Markus Maier 001; Matthias Hein 001; Ulrike von Luxburg (ALT, 2007)

Consistent Minimization of Clustering Objective Functions. - Ulrike von Luxburg; Sébastien Bubeck; Stefanie Jegelka; Michael Kaufmann 001 (NIPS, 2007)

Graph Laplacians and their Convergence on Random Neighborhood Graphs. - Matthias Hein 001; Jean-Yves Audibert; Ulrike von Luxburg (J. Mach. Learn. Res., 2007)

A tutorial on spectral clustering. - Ulrike von Luxburg (Stat. Comput., 2007)

Relating Clustering Stability to Properties of Cluster Boundaries. - Shai Ben-David; Ulrike von Luxburg (COLT, 2008)

Influence of graph construction on graph-based clustering measures. - Markus Maier 001; Ulrike von Luxburg; Matthias Hein 001 (NIPS, 2008)

Generalized Clustering via Kernel Embeddings. - Stefanie Jegelka; Arthur Gretton; Bernhard Schölkopf; Bharath K. Sriperumbudur; Ulrike von Luxburg (KI, 2009)

Clustering Stability: An Overview. - Ulrike von Luxburg (Found. Trends Mach. Learn., 2009)

Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions. - Sébastien Bubeck; Ulrike von Luxburg (J. Mach. Learn. Res., 2009)

Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters. - Markus Maier 001; Matthias Hein 001; Ulrike von Luxburg (Theor. Comput. Sci., 2009)

Multi-agent Random Walks for Local Clustering on Graphs. - Morteza Alamgir; Ulrike von Luxburg (ICDM, 2010)

Getting lost in space: Large sample analysis of the resistance distance. - Ulrike von Luxburg; Agnes Radl; Matthias Hein 001 (NIPS, 2010)

Hitting times, commute distances and the spectral gap for large random geometric graphs - Ulrike von Luxburg; Agnes Radl; Matthias Hein 001 (CoRR, 2010)

Risk-Based Generalizations of f-divergences. - Dario García-García; Ulrike von Luxburg; Raúl Santos-Rodríguez (ICML, 2011)

Pruning nearest neighbor cluster trees. - Samory Kpotufe; Ulrike von Luxburg (ICML, 2011)

Phase transition in the family of p-resistances. - Morteza Alamgir; Ulrike von Luxburg (NIPS, 2011)

How the result of graph clustering methods depends on the construction of the graph - Markus Maier 001; Ulrike von Luxburg; Matthias Hein 001 (CoRR, 2011)

Preface. - Sham M. Kakade; Ulrike von Luxburg (COLT, 2011)

Statistical Learning Theory: Models, Concepts, and Results. - Ulrike von Luxburg; Bernhard Schölkopf (Inductive Logic, 2011)

Shortest path distance in random k-nearest neighbor graphs. - Morteza Alamgir; Ulrike von Luxburg (ICML, 2012)

Clustering: Science or Art? - Ulrike von Luxburg; Robert C. Williamson; Isabelle Guyon (ICML Unsupervised and Transfer Learning, 2012)

Density estimation from unweighted k-nearest neighbor graphs: a roadmap. - Ulrike von Luxburg; Morteza Alamgir (NIPS, 2013)

Density-preserving quantization with application to graph downsampling. - Morteza Alamgir; Gábor Lugosi; Ulrike von Luxburg (COLT, 2014)

Uniqueness of Ordinal Embedding. - Matthäus Kleindessner; Ulrike von Luxburg (COLT, 2014)

The f-Adjusted Graph Laplacian: a Diagonal Modification with a Geometric Interpretation. - Sven Kurras; Ulrike von Luxburg; Gilles Blanchard (ICML, 2014)

Local Ordinal Embedding. - Yoshikazu Terada; Ulrike von Luxburg (ICML, 2014)

Hitting and commute times in large random neighborhood graphs. - Ulrike von Luxburg; Agnes Radl; Matthias Hein 001 (J. Mach. Learn. Res., 2014)

Consistent Procedures for Cluster Tree Estimation and Pruning. - Kamalika Chaudhuri; Sanjoy Dasgupta; Samory Kpotufe; Ulrike von Luxburg (IEEE Trans. Inf. Theory, 2014)

Dimensionality estimation without distances. - Matthäus Kleindessner; Ulrike von Luxburg (AISTATS, 2015)

Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines. - Mehdi S. M. Sajjadi; Morteza Alamgir; Ulrike von Luxburg (L@S, 2016)

Kernel functions based on triplet similarity comparisons. - Matthäus Kleindessner; Ulrike von Luxburg (CoRR, 2016)

Foundations of Unsupervised Learning (Dagstuhl Seminar 16382). - Maria-Florina Balcan; Shai Ben-David; Ruth Urner; Ulrike von Luxburg (Dagstuhl Reports, 2016)

Feasibility of Active Machine Learning for Multiclass Compound Classification. - Tobias Lang 001; Florian Flachsenberg; Ulrike von Luxburg; Matthias Rarey (J. Chem. Inf. Model., 2016)

Comparison-Based Nearest Neighbor Search. - Siavash Haghiri; Debarghya Ghoshdastidar; Ulrike von Luxburg (AISTATS, 2017)

Two-Sample Tests for Large Random Graphs Using Network Statistics. - Debarghya Ghoshdastidar; Maurilio Gutzeit; Alexandra Carpentier; Ulrike von Luxburg (COLT, 2017)

Kernel functions based on triplet comparisons. - Matthäus Kleindessner; Ulrike von Luxburg (NIPS, 2017)

Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis. - Matthäus Kleindessner; Ulrike von Luxburg (J. Mach. Learn. Res., 2017)

Comparison-Based Random Forests. - Siavash Haghiri; Damien Garreau; Ulrike von Luxburg (ICML, 2018)

Practical Methods for Graph Two-Sample Testing. - Debarghya Ghoshdastidar; Ulrike von Luxburg (NeurIPS, 2018)

When do random forests fail? - Cheng Tang; Damien Garreau; Ulrike von Luxburg (NeurIPS, 2018)

Measures of distortion for machine learning. - Leena Chennuru Vankadara; Ulrike von Luxburg (NeurIPS, 2018)

Design and Analysis of the NIPS 2016 Review Process. - Nihar B. Shah; Behzad Tabibian; Krikamol Muandet; Isabelle Guyon; Ulrike von Luxburg (J. Mach. Learn. Res., 2018)

Boosting for Comparison-Based Learning. - Michaël Perrot; Ulrike von Luxburg (IJCAI, 2019)

Foundations of Comparison-Based Hierarchical Clustering. - Debarghya Ghoshdastidar; Michaël Perrot; Ulrike von Luxburg (NeurIPS, 2019)

Comparison-Based Framework for Psychophysics: Lab versus Crowdsourcing. - Siavash Haghiri; Felix A. Wichmann; Ulrike von Luxburg (CoRR, 2019)

Uncertainty Estimates for Ordinal Embeddings. - Michael Lohaus; Philipp Hennig; Ulrike von Luxburg (CoRR, 2019)

Estimation of perceptual scales using ordinal embedding. - Siavash Haghiri; Felix A. Wichmann; Ulrike von Luxburg (CoRR, 2019)

Large scale representation learning from triplet comparisons. - Siavash Haghiri; Leena Chennuru Vankadara; Ulrike von Luxburg (CoRR, 2019)

Explaining the Explainer: A First Theoretical Analysis of LIME. - Damien Garreau; Ulrike von Luxburg (AISTATS, 2020)

Too Relaxed to Be Fair. - Michael Lohaus; Michaël Perrot; Ulrike von Luxburg (ICML, 2020)

NetGAN without GAN: From Random Walks to Low-Rank Approximations. - Luca Rendsburg; Holger Heidrich; Ulrike von Luxburg (ICML, 2020)

Tangles: From Weak to Strong Clustering. - Christian Elbracht; Diego Fioravanti; Solveig Klepper; Jakob Kneip; Luca Rendsburg; Maximilian Teegen; Ulrike von Luxburg (CoRR, 2020)

When Humans and Machines Make Joint Decisions: A Non-Symmetric Bandit Model. - Sebastian Bordt; Ulrike von Luxburg (CoRR, 2020)

Looking deeper into LIME. - Damien Garreau; Ulrike von Luxburg (CoRR, 2020)

Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models. - Leena C. Vankadara; Sebastian Bordt; Ulrike von Luxburg; Debarghya Ghoshdastidar (AISTATS, 2021)

Specialists Outperform Generalists in Ensemble Classification. - Sascha Meyen; Frieder Göppert; Helen Alber; Ulrike von Luxburg; Volker H. Franz (CoRR, 2021)

A Bandit Model for Human-Machine Decision Making with Private Information and Opacity. - Sebastian Bordt; Ulrike von Luxburg (AISTATS, 2022)

Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. - Luca Rendsburg; Agustinus Kristiadi; Philipp Hennig; Ulrike von Luxburg (AISTATS, 2022)

Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts. - Sebastian Bordt; Michèle Finck; Eric Raidl; Ulrike von Luxburg (FAccT, 2022)

Interpolation and Regularization for Causal Learning. - Leena Chennuru Vankadara; Luca Rendsburg; Ulrike von Luxburg; Debarghya Ghoshdastidar (NeurIPS, 2022)

A Consistent Estimator for Confounding Strength. - Luca Rendsburg; Leena Chennuru Vankadara; Debarghya Ghoshdastidar; Ulrike von Luxburg (CoRR, 2022)

Pitfalls of Climate Network Construction: A Statistical Perspective. - Moritz Haas; Bedartha Goswami; Ulrike von Luxburg (CoRR, 2022)

Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382). - Philipp Berens; Kyle Cranmer; Neil D. Lawrence; Ulrike von Luxburg; Jessica Montgomery (Dagstuhl Reports, 2022)

From Shapley Values to Generalized Additive Models and back. - Sebastian Bordt; Ulrike von Luxburg (AISTATS, 2023)

The Manifold Hypothesis for Gradient-Based Explanations. - Sebastian Bordt; Uddeshya Upadhyay; Zeynep Akata; Ulrike von Luxburg (CVPR Workshops, 2023)

Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension. - Moritz Haas; David Holzmüller; Ulrike von Luxburg; Ingo Steinwart (NeurIPS, 2023)

AI for Science: An Emerging Agenda. - Philipp Berens; Kyle Cranmer; Neil D. Lawrence; Ulrike von Luxburg; Jessica Montgomery (CoRR, 2023)

ChatGPT Participates in a Computer Science Exam. - Sebastian Bordt; Ulrike von Luxburg (CoRR, 2023)

Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees. - Solveig Klepper; Christian Elbracht; Diego Fioravanti; Jakob Kneip; Luca Rendsburg; Maximilian Teegen; Ulrike von Luxburg (J. Mach. Learn. Res., 2023)

Insights into Ordinal Embedding Algorithms: A Systematic Evaluation. - Leena Chennuru Vankadara; Michael Lohaus; Siavash Haghiri; Faiz Ul Wahab; Ulrike von Luxburg (J. Mach. Learn. Res., 2023)

Relating graph auto-encoders to linear models. - Solveig Klepper; Ulrike von Luxburg (Trans. Mach. Learn. Res., 2023)

Auditing Local Explanations is Hard. - Robi Bhattacharjee; Ulrike von Luxburg (NeurIPS, 2024)

Statistics without Interpretation: A Sober Look at Explainable Machine Learning. - Sebastian Bordt; Ulrike von Luxburg (CoRR, 2024)

How much can we forget about Data Contamination? - Sebastian Bordt; Suraj Srinivas; Valentyn Boreiko; Ulrike von Luxburg (CoRR, 2024)

Disentangling Interactions and Dependencies in Feature Attribution. - Gunnar König; Eric Günther; Ulrike von Luxburg (CoRR, 2024)

cblearn: Comparison-based Machine Learning in Python. - David-Elias Künstle; Ulrike von Luxburg (J. Open Source Softw., 2024)

Disentangling Interactions and Dependencies in Feature Attributions. - Gunnar König; Eric Günther; Ulrike von Luxburg (AISTATS, 2025)

How to safely discard features based on aggregate SHAP values. - Robi Bhattacharjee; Karolin Frohnapfel; Ulrike von Luxburg (COLT, 2025)

On the Surprising Effectiveness of Large Learning Rates under Standard Width Scaling. - Moritz Haas; Sebastian Bordt; Ulrike von Luxburg; Leena Chennuru Vankadara (CoRR, 2025)

Performative Validity of Recourse Explanations. - Gunnar König; Hidde Fokkema; Timo Freiesleben; Celestine Mendler-Dünner; Ulrike von Luxburg (CoRR, 2025)

Informative Post-Hoc Explanations Only Exist for Simple Functions. - Eric Günther; Balázs Szabados; Robi Bhattacharjee; Sebastian Bordt; Ulrike von Luxburg (CoRR, 2025)