← Authors

Ulrike Von Luxburg

29 papers · 2004–2025 · 6 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (6) πŸŒ‰ Interdisciplinary Bridge 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (14) 🐝 Cross-Pollinator (13) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (21) 🌟 Keyword Trendsetter Combo (4) 🌱 Topic Pioneer πŸ† Keyword Champion (2) ❓ The Questioner (2) πŸ’Ž Century Club (29) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (132) πŸ”₯ Unstoppable (9)

Conferences

JMLR (9) NIPS (7) AISTATS (6) ICML (4) COLT (2) IJCAI (1)

Top co-authors

Debarghya Ghoshdastidar (5) Sebastian Bordt (5) Luca Rendsburg (3) MichaΓ«l Perrot (3) Olivier Bousquet (2) Matthias Hein (2) Leena Chennuru Vankadara (2) Robi Bhattacharjee (2) Michael Lohaus (2) MatthΓ€us Kleindessner (2)

Research topics

Statistics (1)

Keywords

metric learning (3) random walk (3) graph theory (2) ordinal embedding (2) comparison-based learning (2) hierarchical clustering (2) machine learning (2) graph analysis (2) metric space (2) triplet comparison (2) support vector machine (2) unsupervised learning (2) nearest neighbor graph (2) ensemble learning (1) graph laplacian (1) statistical consistency (1) spectral clustering (1) manifold learning (1) semi-supervised learning (1) dimensionality reduction (1)

Papers

Position: Rethinking Explainable Machine Learning as Applied Statistics ICML 2025
How Much Can We Forget about Data Contamination? ICML 2025
How to safely discard features based on aggregate SHAP values COLT 2025
Disentangling Interactions and Dependencies in Feature Attributions AISTATS 2025
Auditing Local Explanations is Hard NIPS 2024
Insights into Ordinal Embedding Algorithms: A Systematic Evaluation JMLR 2023
From Shapley Values to Generalized Additive Models and back AISTATS 2023
Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees JMLR 2023
A Bandit Model for Human-Machine Decision Making with Private Information and Opacity AISTATS 2022
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference AISTATS 2022
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models AISTATS 2021
Too Relaxed to Be Fair ICML 2020
NetGAN without GAN: From Random Walks to Low-Rank Approximations ICML 2020
Boosting for Comparison-Based Learning IJCAI 2019
Foundations of Comparison-Based Hierarchical Clustering NIPS 2019
Measures of distortion for machine learning NIPS 2018
Practical Methods for Graph Two-Sample Testing NIPS 2018
Design and Analysis of the NIPS 2016 Review Process JMLR 2018
When do random forests fail? NIPS 2018
Comparison-Based Nearest Neighbor Search AISTATS 2017
Two-Sample Tests for Large Random Graphs Using Network Statistics COLT 2017
Kernel functions based on triplet comparisons NIPS 2017
Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis JMLR 2017
Hitting and Commute Times in Large Random Neighborhood Graphs JMLR 2014
Density estimation from unweighted k-nearest neighbor graphs: a roadmap NIPS 2013
Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions JMLR 2009
Graph Laplacians and their Convergence on Random Neighborhood Graphs JMLR 2007
Distance-Based Classification with Lipschitz Functions JMLR 2004
A Compression Approach to Support Vector Model Selection JMLR 2004