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Ulrike von Luxburg

Ulrike von Luxburg

D-Index & Metrics

Computer Science

D-Index
42
Citations
25793
World Ranking
8133
National Ranking
396

Research.com Recognitions

  • 2020 - German National Academy of Sciences Leopoldina - Deutsche Akademie der Naturforscher Leopoldina – Nationale Akademie der Wissenschaften Informatics

Overview

Ulrike von Luxburg is affiliated with the University of Tübingen in Germany and has a research focus primarily in computer science. Their work spans several subfields, including artificial intelligence, cognitive neuroscience, statistics and probability, management science and operations research, and social psychology.

The scientist has contributed extensively to topics such as explainable artificial intelligence (XAI), neural and behavioral psychology studies, visual perception and processing mechanisms, machine learning and data classification, adversarial robustness in machine learning, Gaussian processes and Bayesian inference, as well as broader statistical methods and Bayesian inference.

Frequent publication venues include the arXiv (Cornell University), where 23 of their works have appeared, alongside journals such as the Journal of Vision with four publications. They have also published in the 2022 ACM Conference on Fairness, Accountability, and Transparency, the Journal of Experimental Psychology General, and Cognitive Research Principles and Implications.

Among recent papers, their works include:

  • Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts (2022), presented at the 2022 ACM Conference on Fairness, Accountability, and Transparency
  • Advancing research on unconscious priming: When can scientists claim an indirect task advantage? (2021), published in the Journal of Experimental Psychology General
  • Explaining the Explainer: A First Theoretical Analysis of LIME (2020), available on arXiv (Cornell University)
  • ChatGPT Participates in a Computer Science Exam (2023), available on arXiv (Cornell University)
  • Group decisions based on confidence weighted majority voting (2021), published in Cognitive Research Principles and Implications

Ulrike von Luxburg has collaborated frequently with several co-authors, including Sebastian Bordt, Sascha Meyen, Volker H. Franz, Leena Chennuru Vankadara, and Luca Rendsburg, with their co-author counts varying from five to eleven collaborations.

The scientist received recognition from the German National Academy of Sciences Leopoldina in 2020 in the field of informatics.

Best Publications

  • A tutorial on spectral clustering

    Ulrike Luxburg

  • Proceedings of the 28th International Conference on Machine Learning, ICML 2011

    Darío García-García;Ulrike Von Luxburg;Raul Santos-Rodriguez

  • Consistency of spectral clustering

    U von Luxburg;M Belkin;O Bousquet

  • From graphs to manifolds – weak and strong pointwise consistency of graph laplacians

    Matthias Hein;Jean-Yves Audibert;Ulrike von Luxburg

  • Clustering Stability: An Overview

    Ulrike von Luxburg

  • Graph Laplacians and their Convergence on Random Neighborhood Graphs

    Matthias Hein;Jean-Yves Audibert;Ulrike von Luxburg

  • A sober look at clustering stability

    Shai Ben-David;Ulrike von Luxburg;Dávid Pál

  • Advanced Lectures on Machine Learning

    O Bousquet;U von Luxburg;G Rätsch

  • Consistency of spectral clustering

    Ulrike von Luxburg;Mikhail Belkin;Olivier Bousquet

  • Influence of graph construction on graph-based clustering measures

    Markus Maier;Ulrike V. Luxburg;Matthias Hein

  • Distance--Based Classification with Lipschitz Functions

    Ulrike von Luxburg;Olivier Bousquet

  • Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters

    Markus Maier;Matthias Hein;Ulrike von Luxburg

  • Limits of Spectral Clustering

    Ulrike V. Luxburg;Olivier Bousquet;Mikhail Belkin

  • Getting lost in space: Large sample analysis of the resistance distance

    Ulrike V. Luxburg;Agnes Radl;Matthias Hein

  • Hitting and commute times in large random neighborhood graphs

    Ulrike Von Luxburg;Agnes Radl;Matthias Hein

  • Explaining the Explainer: A First Theoretical Analysis of LIME

    Damien Garreau;Damien Garreau;Ulrike von Luxburg;Ulrike von Luxburg

  • Advanced lectures on machine learning : ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003 : revised lectures

    Olivier Bousquet;Ulrike Von Luxburg;Gunnar Rätsch

  • Towards a Statistical Theory of Clustering

    Ulrike von Luxburg;Shai Ben-David;Fraunhofer Ipsi

  • Getting lost in space: large sample analysis of the commute distance

    Ulrike von Luxburg;Agnes Radl;Matthias Hein

  • Local Ordinal Embedding

    Yoshikazu Terada;Yoshikazu Terada;Ulrike von Luxburg

  • Clustering: science or art?

    Ulrike Von Luxburg;Robert C. Williamson;Isabelle Guyon

  • Statistical Learning Theory: Models, Concepts, and Results

    Ulrike von Luxburg;Bernhard Schoelkopf

Frequent Co-Authors

Matthias Hein
Matthias Hein University of Tübingen
Olivier Bousquet
Olivier Bousquet Google (United States)
Sébastien Bubeck
Sébastien Bubeck Microsoft (United States)
Mikhail Belkin
Mikhail Belkin University of California, San Diego
Shai Ben-David
Shai Ben-David University of Waterloo
Isabelle Guyon
Isabelle Guyon University of Paris-Saclay
Felix A. Wichmann
Felix A. Wichmann University of Tübingen
Jean-Yves Audibert
Jean-Yves Audibert Capital Fund Management (France)
Michael Kaufmann
Michael Kaufmann University of Tübingen

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