Ulrike Von Luxburg
29 papers · 2004–2025 · 6 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π§ Keyword Pioneer πΊοΈ Taxonomy Completionist (14) π Interdisciplinary Bridge π Conference Polyglot (6)
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π
Academic Marathon
(21)
π
Keyword Trendsetter Combo
(4)
π±
Topic Pioneer
π
Keyword Champion
(2)
β
The Questioner
(2)
π
Century Club
(29)
π
Trend Setter
ποΈ
Keyword Collector
(132)
π₯
Unstoppable
(9)
Conferences
JMLR (9)
NIPS (7)
AISTATS (6)
ICML (4)
COLT (2)
IJCAI (1)
Top co-authors
Research topics
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