World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
65
Citations
88759
World Ranking
2377
National Ranking
14

Overview

Kurt Hornik is affiliated with the Vienna University of Economics and Business in Austria. Their research spans multiple fields, primarily focusing on computer science and mathematics. Within these broader domains, their work is concentrated on subfields such as artificial intelligence, statistics and probability, applied mathematics, algebra and number theory, and finance.

Their scholarly output includes publications in a range of respected venues. Frequent publication platforms include the Journal of Statistical Software, The R Journal, arXiv (Cornell University), Journal of Computational and Graphical Statistics, and Remote Sensing. They have authored papers covering diverse research topics that intersect statistical methodology and computational techniques.

Key topics addressed in their body of work encompass:

  • Statistical Methods and Inference
  • Advanced Statistical Methods and Models
  • Mathematical Functions and Polynomials
  • Advanced Mathematical Identities
  • Bayesian Methods and Mixture Models
  • Sports Analytics and Performance
  • Statistical Methods and Bayesian Inference

Frequent collaborators include Lukas Sablica, Laura Vana, Bettina Grün, Achim Zeileis, and Rainer Hirk, reflecting a network of coauthors engaged in related research areas.

Representative recent papers authored or co-authored by Kurt Hornik include:

  • colorspace: A Toolbox for Manipulating and Assessing Colors and Palettes (2020), published in Journal of Statistical Software
  • mvord: An R Package for Fitting Multivariate Ordinal Regression Models (2020), published in Journal of Statistical Software
  • ROI: An Extensible R Optimization Infrastructure (2020), published in Journal of Statistical Software
  • Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery (2020), published in Remote Sensing
  • A comparison of optimization solvers for log binomial regression including conic programming (2021), published in Computational Statistics

The scope of their work combines theoretical and applied perspectives, integrating statistical inference methods with computational tools and machine learning. The range of topics covered spans from Bayesian mixture models to semantic segmentation in satellite imagery, highlighting a multidisciplinary approach.

Best Publications

  • Multilayer feedforward networks are universal approximators

    K. Hornik;M. Stinchcombe;H. White

  • Bioconductor: open software development for computational biology and bioinformatics

    Robert C Gentleman;Vincent J Carey;Douglas M. Bates;B.M. Bolstad

  • Approximation capabilities of multilayer feedforward networks

    Kurt Hornik

  • Unbiased Recursive Partitioning: A Conditional Inference Framework

    Torsten Hothorn;Kurt Hornik;Achim Zeileis

  • Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks

    Kurt Hornik;Maxwell Stinchcombe;Halbert White

  • kernlab - An S4 Package for Kernel Methods in R

    Alexandros Karatzoglou;Alexandros Smola;Kurt Hornik;Achim Zeileis

  • strucchange. An R package for testing for structural change in linear regression models.

    Achim Zeileis;Friedrich Leisch;Kurt Hornik;Christian Kleiber

  • Neural networks and principal component analysis: learning from examples without local minima

    P. Baldi;K. Hornik

  • Implementing a class of permutation pests: the coin package

    Torsten Hothorn;Kurt Hornik;Mark A. van de Wiel;Achim Zeileis

  • Text Mining Infrastructure in R

    Ingo Feinerer;Kurt Hornik;David Meyer

  • topicmodels: An R Package for Fitting Topic Models

    Bettina Grün;Kurt Hornik

  • Testing and dating of structural changes in practice

    Achim Zeileis;Christian Kleiber;Walter Krämer;Kurt Hornik

  • The support vector machine under test

    David Meyer;Friedrich Leisch;Kurt Hornik

  • FEED FORWARD NETWORKS ARE UNIVERSAL APPROXIMATORS

    K Hornik;M Stinchcombe;H White

  • A Lego System for Conditional Inference

    Torsten Hothorn;Kurt Hornik;Mark A van de Wiel;Achim Zeileis

  • Model-Based Recursive Partitioning

    Achim Zeileis;Torsten Hothorn;Kurt Hornik

  • Misc Functions of the Department of Statistics (e1071), TU Wien

    David Meyer;Evgenia Dimitriadou;Kurt Hornik;Andreas Weingessel

  • Misc Functions of the Department of Statistics, ProbabilityTheory Group (Formerly: E1071), TU Wien

    David Meyer;Evgenia Dimitriadou;Kurt Hornik;Andreas Weingessel

  • Support Vector Machines in R

    Alexandros Karatzoglou;David Meyer;Kurt Hornik

  • Some new results on neural network approximation

    K. Hornik

  • Bioconductor: Open Software Development for Computational Biology and Bioinformatics

    Kurt Hornik;Robert C. Gentleman;Vincent J. Carey;Douglas M. Bates

Frequent Co-Authors

Friedrich Leisch
Friedrich Leisch BOKU University
Achim Zeileis
Achim Zeileis University of Innsbruck
Siegfried Kasper
Siegfried Kasper Medical University of Vienna
Werner Sieghart
Werner Sieghart Medical University of Vienna
Horst Bischof
Horst Bischof Graz University of Technology
Nicole Praschak-Rieder
Nicole Praschak-Rieder Medical University of Vienna
Alexandros Karatzoglou
Alexandros Karatzoglou Google (United States)
Halbert White
Halbert White University of California, San Diego
Pierre Baldi
Pierre Baldi University of California, Irvine
Susanne Asenbaum
Susanne Asenbaum Medical University of Vienna

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