Ulrike von Luxburg

119 papers A* 29A 8B 2Misc 1Journal 62Unranked 12

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Using predictive multiplicity to measure individual performance within the AI Act. CoRR Karolin Frohnapfel, Mara Seyfert, Sebastian Bordt, Ulrike von Luxburg, Kristof Meding
2025	A	`conf`	Disentangling Interactions and Dependencies in Feature Attributions. AISTATS Gunnar König, Eric Günther, Ulrike von Luxburg
2025	A*	`conf`	How Much Can We Forget about Data Contamination? ICML Sebastian Bordt, Suraj Srinivas, Valentyn Boreiko, Ulrike von Luxburg
2025	A*	`conf`	How to safely discard features based on aggregate SHAP values. COLT Robi Bhattacharjee, Karolin Frohnapfel, Ulrike von Luxburg
2025	J	`jnl`	How to safely discard features based on aggregate SHAP values. CoRR Robi Bhattacharjee, Karolin Frohnapfel, Ulrike von Luxburg
2025	J	`jnl`	Informative Post-Hoc Explanations Only Exist for Simple Functions. CoRR Eric Günther, Balázs Szabados, Robi Bhattacharjee, Sebastian Bordt, Ulrike von Luxburg
2025	J	`jnl`	On the Surprising Effectiveness of Large Learning Rates under Standard Width Scaling. CoRR Moritz Haas, Sebastian Bordt, Ulrike von Luxburg, Leena Chennuru Vankadara
2025	J	`jnl`	Performative Validity of Recourse Explanations. CoRR Gunnar König, Hidde Fokkema, Timo Freiesleben, Celestine Mendler-Dünner, Ulrike von Luxburg
2025	—	`conf`	Position: Rethinking Explainable Machine Learning as Applied Statistics. ICML (Position Papers) Sebastian Bordt, Eric Raidl, Ulrike von Luxburg
2024	A*	`conf`	Auditing Local Explanations is Hard. NeurIPS Robi Bhattacharjee, Ulrike von Luxburg
2024	J	`jnl`	Auditing Local Explanations is Hard. CoRR Robi Bhattacharjee, Ulrike von Luxburg
2024	J	`jnl`	Disentangling Interactions and Dependencies in Feature Attribution. CoRR Gunnar König, Eric Günther, Ulrike von Luxburg
2024	J	`jnl`	How much can we forget about Data Contamination? CoRR Sebastian Bordt, Suraj Srinivas, Valentyn Boreiko, Ulrike von Luxburg
2024	J	`jnl`	Statistics without Interpretation: A Sober Look at Explainable Machine Learning. CoRR Sebastian Bordt, Ulrike von Luxburg
2024	J	`jnl`	cblearn: Comparison-based Machine Learning in Python. J. Open Source Softw. David-Elias Künstle, Ulrike von Luxburg
2023	J	`jnl`	AI for Science: An Emerging Agenda. CoRR Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery
2023	J	`jnl`	ChatGPT Participates in a Computer Science Exam. CoRR Sebastian Bordt, Ulrike von Luxburg
2023	J	`jnl`	Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees. J. Mach. Learn. Res. Solveig Klepper, Christian Elbracht, Diego Fioravanti, Jakob Kneip, Luca Rendsburg, Maximilian Teegen, Ulrike von Luxburg
2023	A	`conf`	From Shapley Values to Generalized Additive Models and back. AISTATS Sebastian Bordt, Ulrike von Luxburg
2023	J	`jnl`	Insights into Ordinal Embedding Algorithms: A Systematic Evaluation. J. Mach. Learn. Res. Leena Chennuru Vankadara, Michael Lohaus, Siavash Haghiri, Faiz Ul Wahab, Ulrike von Luxburg
2023	A*	`conf`	Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension. NeurIPS Moritz Haas, David Holzmüller, Ulrike von Luxburg, Ingo Steinwart
2023	J	`jnl`	Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension. CoRR Moritz Haas, David Holzmüller, Ulrike von Luxburg, Ingo Steinwart
2023	J	`jnl`	Relating graph auto-encoders to linear models. Trans. Mach. Learn. Res. Solveig Klepper, Ulrike von Luxburg
2023	—	`conf`	The Manifold Hypothesis for Gradient-Based Explanations. CVPR Workshops Sebastian Bordt, Uddeshya Upadhyay, Zeynep Akata, Ulrike von Luxburg
2023	—	`conf`	The Manifold Hypothesis for Gradient-Based Explanations. XAI4CV Sebastian Bordt, Uddeshya Upadhyay, Zeynep Akata, Ulrike von Luxburg
2022	A	`conf`	A Bandit Model for Human-Machine Decision Making with Private Information and Opacity. AISTATS Sebastian Bordt, Ulrike von Luxburg
2022	J	`jnl`	A Consistent Estimator for Confounding Strength. CoRR Luca Rendsburg, Leena Chennuru Vankadara, Debarghya Ghoshdastidar, Ulrike von Luxburg
2022	A	`conf`	Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. AISTATS Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg
2022	J	`jnl`	Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. CoRR Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg
2022	J	`jnl`	From Shapley Values to Generalized Additive Models and back. CoRR Sebastian Bordt, Ulrike von Luxburg
2022	A*	`conf`	Interpolation and Regularization for Causal Learning. NeurIPS Leena Chennuru Vankadara, Luca Rendsburg, Ulrike von Luxburg, Debarghya Ghoshdastidar
2022	J	`jnl`	Interpolation and Regularization for Causal Learning. CoRR Leena Chennuru Vankadara, Luca Rendsburg, Ulrike von Luxburg, Debarghya Ghoshdastidar
2022	J	`jnl`	Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382). Dagstuhl Reports Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery
2022	J	`jnl`	Pitfalls of Climate Network Construction: A Statistical Perspective. CoRR Moritz Haas, Bedartha Goswami, Ulrike von Luxburg
2022	—	`conf`	Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts. FAccT Sebastian Bordt, Michèle Finck, Eric Raidl, Ulrike von Luxburg
2022	J	`jnl`	Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts. CoRR Sebastian Bordt, Michèle Finck, Eric Raidl, Ulrike von Luxburg
2022	J	`jnl`	Relating graph auto-encoders to linear models. CoRR Solveig Klepper, Ulrike von Luxburg
2022	J	`jnl`	The Manifold Hypothesis for Gradient-Based Explanations. CoRR Sebastian Bordt, Uddeshya Upadhyay, Zeynep Akata, Ulrike von Luxburg
2021	A	`conf`	Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models. AISTATS Leena C. Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar
2021	J	`jnl`	Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models. CoRR Leena C. Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar
2021	J	`jnl`	Specialists Outperform Generalists in Ensemble Classification. CoRR Sascha Meyen, Frieder Göppert, Helen Alber, Ulrike von Luxburg, Volker H. Franz
2020	A	`conf`	Explaining the Explainer: A First Theoretical Analysis of LIME. AISTATS Damien Garreau, Ulrike von Luxburg
2020	J	`jnl`	Explaining the Explainer: A First Theoretical Analysis of LIME. CoRR Damien Garreau, Ulrike von Luxburg
2020	J	`jnl`	Looking deeper into LIME. CoRR Damien Garreau, Ulrike von Luxburg
2020	A*	`conf`	NetGAN without GAN: From Random Walks to Low-Rank Approximations. ICML Luca Rendsburg, Holger Heidrich, Ulrike von Luxburg
2020	J	`jnl`	Tangles: From Weak to Strong Clustering. CoRR Christian Elbracht, Diego Fioravanti, Solveig Klepper, Jakob Kneip, Luca Rendsburg, Maximilian Teegen, Ulrike von Luxburg
2020	A*	`conf`	Too Relaxed to Be Fair. ICML Michael Lohaus, Michaël Perrot, Ulrike von Luxburg
2020	J	`jnl`	When Humans and Machines Make Joint Decisions: A Non-Symmetric Bandit Model. CoRR Sebastian Bordt, Ulrike von Luxburg
2019	A*	`conf`	Boosting for Comparison-Based Learning. IJCAI Michaël Perrot, Ulrike von Luxburg
2019	J	`jnl`	Comparison-Based Framework for Psychophysics: Lab versus Crowdsourcing. CoRR Siavash Haghiri, Felix A. Wichmann, Ulrike von Luxburg
2019	J	`jnl`	Estimation of perceptual scales using ordinal embedding. CoRR Siavash Haghiri, Felix A. Wichmann, Ulrike von Luxburg
2019	A*	`conf`	Foundations of Comparison-Based Hierarchical Clustering. NeurIPS Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg
2019	J	`jnl`	Large scale representation learning from triplet comparisons. CoRR Siavash Haghiri, Leena Chennuru Vankadara, Ulrike von Luxburg
2019	J	`jnl`	Uncertainty Estimates for Ordinal Embeddings. CoRR Michael Lohaus, Philipp Hennig, Ulrike von Luxburg
2018	J	`jnl`	Boosting for Comparison-Based Learning. CoRR Michaël Perrot, Ulrike von Luxburg
2018	A*	`conf`	Comparison-Based Random Forests. ICML Siavash Haghiri, Damien Garreau, Ulrike von Luxburg
2018	J	`jnl`	Comparison-Based Random Forests. CoRR Siavash Haghiri, Damien Garreau, Ulrike von Luxburg
2018	J	`jnl`	Design and Analysis of the NIPS 2016 Review Process. J. Mach. Learn. Res. Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike von Luxburg
2018	J	`jnl`	Foundations of Comparison-Based Hierarchical Clustering. CoRR Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg
2018	A*	`conf`	Measures of distortion for machine learning. NeurIPS Leena Chennuru Vankadara, Ulrike von Luxburg
2018	A*	`conf`	Practical Methods for Graph Two-Sample Testing. NeurIPS Debarghya Ghoshdastidar, Ulrike von Luxburg
2018	J	`jnl`	Practical methods for graph two-sample testing. CoRR Debarghya Ghoshdastidar, Ulrike von Luxburg
2018	A*	`conf`	When do random forests fail? NeurIPS Cheng Tang, Damien Garreau, Ulrike von Luxburg
2017	—	`ed.`	Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, Roman Garnett
2017	J	`jnl`	Comparison Based Nearest Neighbor Search. CoRR Siavash Haghiri, Debarghya Ghoshdastidar, Ulrike von Luxburg
2017	A	`conf`	Comparison-Based Nearest Neighbor Search. AISTATS Siavash Haghiri, Debarghya Ghoshdastidar, Ulrike von Luxburg
2017	J	`jnl`	Design and Analysis of the NIPS 2016 Review Process. CoRR Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike von Luxburg
2017	—	`conf`	Kernel functions based on triplet comparisons. NIPS Matthäus Kleindessner, Ulrike von Luxburg
2017	J	`jnl`	Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis. J. Mach. Learn. Res. Matthäus Kleindessner, Ulrike von Luxburg
2017	A*	`conf`	Two-Sample Tests for Large Random Graphs Using Network Statistics. COLT Debarghya Ghoshdastidar, Maurilio Gutzeit, Alexandra Carpentier, Ulrike von Luxburg
2016	—	`ed.`	Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain NIPS Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, Roman Garnett
2016	J	`jnl`	Feasibility of Active Machine Learning for Multiclass Compound Classification. J. Chem. Inf. Model. Tobias Lang, Florian Flachsenberg, Ulrike von Luxburg, Matthias Rarey
2016	J	`jnl`	Foundations of Unsupervised Learning (Dagstuhl Seminar 16382). Dagstuhl Reports Maria-Florina Balcan, Shai Ben-David, Ruth Urner, Ulrike von Luxburg
2016	J	`jnl`	Kernel functions based on triplet similarity comparisons. CoRR Matthäus Kleindessner, Ulrike von Luxburg
2016	J	`jnl`	Lens depth function and k-relative neighborhood graph: versatile tools for ordinal data analysis. CoRR Matthäus Kleindessner, Ulrike von Luxburg
2016	B	`conf`	Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines. L@S Mehdi S. M. Sajjadi, Morteza Alamgir, Ulrike von Luxburg
2015	A	`conf`	Dimensionality estimation without distances. AISTATS Matthäus Kleindessner, Ulrike von Luxburg
2015	J	`jnl`	Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines. CoRR Mehdi S. M. Sajjadi, Morteza Alamgir, Ulrike von Luxburg
2014	J	`jnl`	Consistent Procedures for Cluster Tree Estimation and Pruning. IEEE Trans. Inf. Theory Kamalika Chaudhuri, Sanjoy Dasgupta, Samory Kpotufe, Ulrike von Luxburg
2014	A*	`conf`	Density-preserving quantization with application to graph downsampling. COLT Morteza Alamgir, Gábor Lugosi, Ulrike von Luxburg
2014	J	`jnl`	Hitting and commute times in large random neighborhood graphs. J. Mach. Learn. Res. Ulrike von Luxburg, Agnes Radl, Matthias Hein
2014	A*	`conf`	Local Ordinal Embedding. ICML Yoshikazu Terada, Ulrike von Luxburg
2014	A*	`conf`	The f-Adjusted Graph Laplacian: a Diagonal Modification with a Geometric Interpretation. ICML Sven Kurras, Ulrike von Luxburg, Gilles Blanchard
2014	A*	`conf`	Uniqueness of Ordinal Embedding. COLT Matthäus Kleindessner, Ulrike von Luxburg
2013	—	`conf`	Density estimation from unweighted k-nearest neighbor graphs: a roadmap. NIPS Ulrike von Luxburg, Morteza Alamgir
2012	—	`conf`	Clustering: Science or Art? ICML Unsupervised and Transfer Learning Ulrike von Luxburg, Robert C. Williamson, Isabelle Guyon
2012	A*	`conf`	Shortest path distance in random k-nearest neighbor graphs. ICML Morteza Alamgir, Ulrike von Luxburg
2011	A*	`ed.`	COLT 2011 - The 24th Annual Conference on Learning Theory, June 9-11, 2011, Budapest, Hungary COLT Sham M. Kakade, Ulrike von Luxburg
2011	J	`jnl`	How the result of graph clustering methods depends on the construction of the graph CoRR Markus Maier, Ulrike von Luxburg, Matthias Hein
2011	—	`conf`	Phase transition in the family of p-resistances. NIPS Morteza Alamgir, Ulrike von Luxburg
2011	A*	`conf`	Preface. COLT Sham M. Kakade, Ulrike von Luxburg
2011	J	`jnl`	Pruning nearest neighbor cluster trees CoRR Samory Kpotufe, Ulrike von Luxburg
2011	A*	`conf`	Pruning nearest neighbor cluster trees. ICML Samory Kpotufe, Ulrike von Luxburg
2011	A*	`conf`	Risk-Based Generalizations of f-divergences. ICML Dario García-García, Ulrike von Luxburg, Raúl Santos-Rodríguez
2011	—	`ch.`	Statistical Learning Theory: Models, Concepts, and Results. Inductive Logic Ulrike von Luxburg, Bernhard Schölkopf
2010	—	`conf`	Getting lost in space: Large sample analysis of the resistance distance. NIPS Ulrike von Luxburg, Agnes Radl, Matthias Hein
2010	J	`jnl`	Hitting times, commute distances and the spectral gap for large random geometric graphs CoRR Ulrike von Luxburg, Agnes Radl, Matthias Hein
2010	A*	`conf`	Multi-agent Random Walks for Local Clustering on Graphs. ICDM Morteza Alamgir, Ulrike von Luxburg
2009	J	`jnl`	Clustering Stability: An Overview. Found. Trends Mach. Learn. Ulrike von Luxburg
2009	Misc	`conf`	Generalized Clustering via Kernel Embeddings. KI Stefanie Jegelka, Arthur Gretton, Bernhard Schölkopf, Bharath K. Sriperumbudur, Ulrike von Luxburg
2009	J	`jnl`	Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions. J. Mach. Learn. Res. Sébastien Bubeck, Ulrike von Luxburg
2009	J	`jnl`	Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters. Theor. Comput. Sci. Markus Maier, Matthias Hein, Ulrike von Luxburg
2008	—	`conf`	Influence of graph construction on graph-based clustering measures. NIPS Markus Maier, Ulrike von Luxburg, Matthias Hein
2008	A*	`conf`	Relating Clustering Stability to Properties of Cluster Boundaries. COLT Shai Ben-David, Ulrike von Luxburg
2007	J	`jnl`	A Tutorial on Spectral Clustering CoRR Ulrike von Luxburg
2007	J	`jnl`	A tutorial on spectral clustering. Stat. Comput. Ulrike von Luxburg
2007	B	`conf`	Cluster Identification in Nearest-Neighbor Graphs. ALT Markus Maier, Matthias Hein, Ulrike von Luxburg
2007	—	`conf`	Consistent Minimization of Clustering Objective Functions. NIPS Ulrike von Luxburg, Sébastien Bubeck, Stefanie Jegelka, Michael Kaufmann
2007	J	`jnl`	Graph Laplacians and their Convergence on Random Neighborhood Graphs. J. Mach. Learn. Res. Matthias Hein, Jean-Yves Audibert, Ulrike von Luxburg
2006	A*	`conf`	A Sober Look at Clustering Stability. COLT Shai Ben-David, Ulrike von Luxburg, Dávid Pál
2006	J	`jnl`	Graph Laplacians and their convergence on random neighborhood graphs CoRR Matthias Hein, Jean-Yves Audibert, Ulrike von Luxburg
2005	A*	`conf`	From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians. COLT Matthias Hein, Jean-Yves Audibert, Ulrike von Luxburg
2004	J	`jnl`	A Compression Approach to Support Vector Model Selection. J. Mach. Learn. Res. Ulrike von Luxburg, Olivier Bousquet, Bernhard Schölkopf
2004	—	`ed.`	Advanced Lectures on Machine Learning, ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures Advanced Lectures on Machine Learning Olivier Bousquet, Ulrike von Luxburg, Gunnar Rätsch
2004	J	`jnl`	Distance-Based Classification with Lipschitz Functions. J. Mach. Learn. Res. Ulrike von Luxburg, Olivier Bousquet
2004	—	`conf`	Limits of Spectral Clustering. NIPS Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin
2004	A*	`conf`	On the Convergence of Spectral Clustering on Random Samples: The Normalized Case. COLT Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin
2004	—	`—`	Statistical learning with similarity and dissimilarity functions. Ulrike von Luxburg
2003	A*	`conf`	Distance-Based Classification with Lipschitz Functions. COLT Ulrike von Luxburg, Olivier Bousquet