H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 31 Citations 13,635 68 World Ranking 7865 National Ranking 388

Research.com Recognitions

Awards & Achievements

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

Overview

What is she best known for?

The fields of study she is best known for:

  • Statistics
  • Artificial intelligence
  • Machine learning

Her primary scientific interests are in Cluster analysis, Discrete mathematics, Correlation clustering, Fuzzy clustering and Spectral clustering. Her research combines Theoretical computer science and Cluster analysis. Her work carried out in the field of Theoretical computer science brings together such families of science as Graph, Linear algebra and Brown clustering.

Ulrike von Luxburg interconnects DBSCAN and Biclustering in the investigation of issues within Brown clustering. She combines subjects such as Stability and Data mining with her study of Correlation clustering. Her biological study spans a wide range of topics, including Algorithm and Laplacian matrix.

Her most cited work include:

  • A tutorial on spectral clustering (6322 citations)
  • From graphs to manifolds – weak and strong pointwise consistency of graph laplacians (257 citations)
  • A sober look at clustering stability (187 citations)

What are the main themes of her work throughout her whole career to date?

Ulrike von Luxburg mostly deals with Combinatorics, Cluster analysis, Discrete mathematics, Algorithm and Artificial intelligence. Her studies deal with areas such as Theoretical computer science, Mathematical optimization and Data mining as well as Cluster analysis. Her work in the fields of Discrete mathematics, such as Graph, Chordal graph and Laplacian matrix, overlaps with other areas such as Resistance distance.

Her research in Algorithm intersects with topics in Embedding, Boosting, Limit and Estimator. Her work carried out in the field of Artificial intelligence brings together such families of science as Simple, Machine learning and Pattern recognition. Her Fuzzy clustering study which covers Correlation clustering that intersects with Stability.

She most often published in these fields:

  • Combinatorics (27.03%)
  • Cluster analysis (26.13%)
  • Discrete mathematics (26.13%)

What were the highlights of her more recent work (between 2018-2021)?

  • Algorithm (25.23%)
  • Embedding (17.12%)
  • Artificial intelligence (17.12%)

In recent papers she was focusing on the following fields of study:

Her primary areas of study are Algorithm, Embedding, Artificial intelligence, Interpretability and Lime. Her Algorithm research incorporates elements of Maximum likelihood, Ordinal number, Scaling and Of the form. Her work in Embedding covers topics such as Representation which are related to areas like Artificial neural network and Data point.

Her work in Artificial intelligence is not limited to one particular discipline; it also encompasses Machine learning. Her Interpretability research is multidisciplinary, incorporating elements of Function, Multiplicative function and Algebraic structure. The various areas that Ulrike von Luxburg examines in her Theoretical computer science study include Classifier, Computational complexity theory, Graph and Fuzzy clustering.

Between 2018 and 2021, her most popular works were:

  • Explaining the Explainer: A First Theoretical Analysis of LIME (17 citations)
  • Two-sample hypothesis testing for inhomogeneous random graphs (8 citations)
  • Foundations of Comparison-Based Hierarchical Clustering (7 citations)

In her most recent research, the most cited papers focused on:

  • Statistics
  • Artificial intelligence
  • Machine learning

Ulrike von Luxburg mainly focuses on Machine learning, Artificial intelligence, Lime, Function and Interpretability. Ulrike von Luxburg has researched Machine learning in several fields, including Object and Reliability. Her research on Artificial intelligence frequently connects to adjacent areas such as Crowdsourcing.

Her Lime studies intersect with other disciplines such as Algorithm, Random forest, Linear function, Range and Limit. Her Function study incorporates themes from Multiplicative function and Algebraic structure.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

A tutorial on spectral clustering

Ulrike Luxburg.
Statistics and Computing (2007)

9571 Citations

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

Matthias Hein;Jean-Yves Audibert;Ulrike von Luxburg.
conference on learning theory (2005)

329 Citations

Consistency of spectral clustering

Ulrike von Luxburg;Mikhail Belkin;Olivier Bousquet.
arXiv: Statistics Theory (2008)

282 Citations

A sober look at clustering stability

Shai Ben-David;Ulrike von Luxburg;Dávid Pál.
conference on learning theory (2006)

274 Citations

Graph Laplacians and their Convergence on Random Neighborhood Graphs

Matthias Hein;Jean-Yves Audibert;Ulrike von Luxburg.
Journal of Machine Learning Research (2007)

264 Citations

Clustering Stability: An Overview

Ulrike von Luxburg.
(2010)

220 Citations

Influence of graph construction on graph-based clustering measures

Markus Maier;Ulrike V. Luxburg;Matthias Hein.
neural information processing systems (2008)

199 Citations

Distance--Based Classification with Lipschitz Functions

Ulrike von Luxburg;Olivier Bousquet.
conference on learning theory (2004)

155 Citations

Limits of Spectral Clustering

Ulrike V. Luxburg;Olivier Bousquet;Mikhail Belkin.
neural information processing systems (2004)

146 Citations

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

Ulrike V. Luxburg;Agnes Radl;Matthias Hein.
neural information processing systems (2010)

140 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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