Ulrike von Luxburg

Affiliations:
  • University of Tübingen, Germany
  • University of Hamburg, Germany (former)


According to our database1, Ulrike von Luxburg authored at least 81 papers between 2004 and 2024.

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Bibliography

2024
How much can we forget about Data Contamination?
CoRR, 2024

Auditing Local Explanations is Hard.
CoRR, 2024

Statistics without Interpretation: A Sober Look at Explainable Machine Learning.
CoRR, 2024

2023
Relating graph auto-encoders to linear models.
Trans. Mach. Learn. Res., 2023

Insights into Ordinal Embedding Algorithms: A Systematic Evaluation.
J. Mach. Learn. Res., 2023

Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees.
J. Mach. Learn. Res., 2023

ChatGPT Participates in a Computer Science Exam.
CoRR, 2023

AI for Science: An Emerging Agenda.
CoRR, 2023

Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

The Manifold Hypothesis for Gradient-Based Explanations.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

From Shapley Values to Generalized Additive Models and back.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382).
Dagstuhl Reports, September, 2022

Pitfalls of Climate Network Construction: A Statistical Perspective.
CoRR, 2022

A Consistent Estimator for Confounding Strength.
CoRR, 2022

Interpolation and Regularization for Causal Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

A Bandit Model for Human-Machine Decision Making with Private Information and Opacity.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Specialists Outperform Generalists in Ensemble Classification.
CoRR, 2021

Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Looking deeper into LIME.
CoRR, 2020

When Humans and Machines Make Joint Decisions: A Non-Symmetric Bandit Model.
CoRR, 2020

Tangles: From Weak to Strong Clustering.
CoRR, 2020

NetGAN without GAN: From Random Walks to Low-Rank Approximations.
Proceedings of the 37th International Conference on Machine Learning, 2020

Too Relaxed to Be Fair.
Proceedings of the 37th International Conference on Machine Learning, 2020

Explaining the Explainer: A First Theoretical Analysis of LIME.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Large scale representation learning from triplet comparisons.
CoRR, 2019

Estimation of perceptual scales using ordinal embedding.
CoRR, 2019

Uncertainty Estimates for Ordinal Embeddings.
CoRR, 2019

Comparison-Based Framework for Psychophysics: Lab versus Crowdsourcing.
CoRR, 2019

Foundations of Comparison-Based Hierarchical Clustering.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Boosting for Comparison-Based Learning.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018
Design and Analysis of the NIPS 2016 Review Process.
J. Mach. Learn. Res., 2018

Measures of distortion for machine learning.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

When do random forests fail?
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Practical Methods for Graph Two-Sample Testing.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Comparison-Based Random Forests.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis.
J. Mach. Learn. Res., 2017

Kernel functions based on triplet comparisons.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Two-Sample Tests for Large Random Graphs Using Network Statistics.
Proceedings of the 30th Conference on Learning Theory, 2017

Comparison-Based Nearest Neighbor Search.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Feasibility of Active Machine Learning for Multiclass Compound Classification.
J. Chem. Inf. Model., 2016

Foundations of Unsupervised Learning (Dagstuhl Seminar 16382).
Dagstuhl Reports, 2016

Kernel functions based on triplet similarity comparisons.
CoRR, 2016

Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines.
Proceedings of the Third ACM Conference on Learning @ Scale, 2016

2015
Dimensionality estimation without distances.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
Consistent Procedures for Cluster Tree Estimation and Pruning.
IEEE Trans. Inf. Theory, 2014

Hitting and commute times in large random neighborhood graphs.
J. Mach. Learn. Res., 2014

Local Ordinal Embedding.
Proceedings of the 31th International Conference on Machine Learning, 2014

The f-Adjusted Graph Laplacian: a Diagonal Modification with a Geometric Interpretation.
Proceedings of the 31th International Conference on Machine Learning, 2014

Uniqueness of Ordinal Embedding.
Proceedings of The 27th Conference on Learning Theory, 2014

Density-preserving quantization with application to graph downsampling.
Proceedings of The 27th Conference on Learning Theory, 2014

2013
Density estimation from unweighted k-nearest neighbor graphs: a roadmap.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012
Clustering: Science or Art?
Proceedings of the Unsupervised and Transfer Learning, 2012

Shortest path distance in random k-nearest neighbor graphs.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Statistical Learning Theory: Models, Concepts, and Results.
Proceedings of the Inductive Logic, 2011

Preface.
Proceedings of the COLT 2011, 2011

How the result of graph clustering methods depends on the construction of the graph
CoRR, 2011

Phase transition in the family of p-resistances.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Pruning nearest neighbor cluster trees.
Proceedings of the 28th International Conference on Machine Learning, 2011

Risk-Based Generalizations of f-divergences.
Proceedings of the 28th International Conference on Machine Learning, 2011

2010
Hitting times, commute distances and the spectral gap for large random geometric graphs
CoRR, 2010

Getting lost in space: Large sample analysis of the resistance distance.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Multi-agent Random Walks for Local Clustering on Graphs.
Proceedings of the ICDM 2010, 2010

2009
Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters.
Theor. Comput. Sci., 2009

Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions.
J. Mach. Learn. Res., 2009

Clustering Stability: An Overview.
Found. Trends Mach. Learn., 2009

Generalized Clustering via Kernel Embeddings.
Proceedings of the KI 2009: Advances in Artificial Intelligence, 2009

2008
Influence of graph construction on graph-based clustering measures.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Relating Clustering Stability to Properties of Cluster Boundaries.
Proceedings of the 21st Annual Conference on Learning Theory, 2008

2007
A tutorial on spectral clustering.
Stat. Comput., 2007

Graph Laplacians and their Convergence on Random Neighborhood Graphs.
J. Mach. Learn. Res., 2007

Consistent Minimization of Clustering Objective Functions.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Cluster Identification in Nearest-Neighbor Graphs.
Proceedings of the Algorithmic Learning Theory, 18th International Conference, 2007

2006
A Sober Look at Clustering Stability.
Proceedings of the Learning Theory, 19th Annual Conference on Learning Theory, 2006

2005
From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians.
Proceedings of the Learning Theory, 18th Annual Conference on Learning Theory, 2005

2004
Statistical learning with similarity and dissimilarity functions.
PhD thesis, 2004

A Compression Approach to Support Vector Model Selection.
J. Mach. Learn. Res., 2004

Distance-Based Classification with Lipschitz Functions.
J. Mach. Learn. Res., 2004

Limits of Spectral Clustering.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

On the Convergence of Spectral Clustering on Random Samples: The Normalized Case.
Proceedings of the Learning Theory, 17th Annual Conference on Learning Theory, 2004


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