World's Best Scientists 2026 revealed!
Robert C. Williamson

Robert C. Williamson

D-Index & Metrics

Computer Science

D-Index
54
Citations
25992
World Ranking
4432
National Ranking
195

Overview

Robert C. Williamson is affiliated with the University of Tübingen in Germany. Their research primarily centers on computer science, with a focus on several subfields including artificial intelligence, management science and operations research, statistics and probability, computational theory and mathematics, and safety research.

The scientist's work covers a range of topics. Notable areas of study include Bayesian modeling and causal inference, explainable artificial intelligence (XAI), risk and portfolio optimization, rough sets and fuzzy logic, ethics and social impacts of AI, statistical methods and inference, and the philosophy and history of science.

Robert C. Williamson has contributed to various publication venues. The most frequent venue is arXiv (Cornell University), with 22 publications. Other venues include Harvard Data Science Review, Chemometrics and Intelligent Laboratory Systems, Computer, and White Rose Research Online (University of Leeds, The University of Sheffield, University of York).

Frequent coauthors in their research include Rabanus Derr, Christian Fröhlich, Benedikt Höltgen, and Zac Cranko.

Recent papers authored or coauthored by Robert C. Williamson and colleagues include:

  • Classification of cow diet based on milk Mid Infrared Spectra: A data analysis competition at the "International Workshop on Spectroscopy and Chemometrics 2022" (2023), Chemometrics and Intelligent Laboratory Systems
  • PAC-Bayesian Bound for the Conditional Value at Risk (2020), arXiv (Cornell University)
  • Assessing AI Fairness in Finance (2022), Computer
  • Information Processing Equalities and the Information-Risk Bridge (2022), arXiv (Cornell University)
  • Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity (2022), arXiv (Cornell University)

Best Publications

  • Estimating the Support of a High-Dimensional Distribution

    Bernhard Schölkopf;John C. Platt;John C. Shawe-Taylor;Alex J. Smola

  • New Support Vector Algorithms

    Bernhard Schölkopf;Alex J. Smola;Robert C. Williamson;Peter L. Bartlett

  • Online learning with kernels

    J. Kivinen;A.J. Smola;R.C. Williamson

  • Support Vector Method for Novelty Detection

    Bernhard Schölkopf;Robert C Williamson;Alex J. Smola;John Shawe-Taylor

  • Structural risk minimization over data-dependent hierarchies

    J. Shawe-Taylor;P.L. Bartlett;R.C. Williamson;M. Anthony

  • Learning the Kernel with Hyperkernels

    Cheng Soon Ong;Alexander J. Smola;Robert C. Williamson

  • Particle filtering algorithms for tracking an acoustic source in a reverberant environment

    D.B. Ward;E.A. Lehmann;R.C. Williamson

  • Theory and design of broadband sensor arrays with frequency invariant far‐field beam patterns

    Darren B. Ward;Rodney A. Kennedy;Robert C. Williamson

  • Shrinking the Tube: A New Support Vector Regression Algorithm

    Bernhard Schölkopf;Peter L. Bartlett;Alex J. Smola;Robert C Williamson

  • The Need for Open Source Software in Machine Learning

    Sören Sonnenburg;Mikio L. Braun;Cheng Soon Ong;Samy Bengio

  • The cost of fairness in binary classification

    Aditya Krishna Menon;Robert C Williamson

  • A PAC analysis of a Bayesian estimator

    John Shawe-Taylor;Robert C. Williamson

  • Learning with symmetric label noise: the importance of being unhinged

    Brendan van Rooyen;Aditya Krishna Menon;Robert C. Williamson

  • Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators

    R.C. Williamson;A.J. Smola;B. Scholkopf

  • Efficient agnostic learning of neural networks with bounded fan-in

    Wee Sun Lee;P.L. Bartlett;R.C. Williamson

  • Support vector regression with automatic accuracy control.

    B Schölkopf;P Bartlett;AJ Smola;R Williamson

  • Fat-shattering and the learnability of real-valued functions

    Peter L. Bartlett;Philip M. Long;Robert C. Williamson

  • Clustering: Science or Art?

    U von Luxburg;R Williamson;I Guyon

  • Composite Binary Losses

    Mark D. Reid;Robert C. Williamson

  • Information, Divergence and Risk for Binary Experiments

    Mark D. Reid;Robert C. Williamson

  • Clustering: science or art?

    Ulrike Von Luxburg;Robert C. Williamson;Isabelle Guyon

Frequent Co-Authors

Alexander J. Smola
Alexander J. Smola Amazon (United States)
Peter L. Bartlett
Peter L. Bartlett University of California, Berkeley
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Rodney A. Kennedy
Rodney A. Kennedy Australian National University
John Shawe-Taylor
John Shawe-Taylor University College London
Aditya Krishna Menon
Aditya Krishna Menon Google (United States)
Richard Nock
Richard Nock Australian National University
Wee Sun Lee
Wee Sun Lee National University of Singapore
Thushara D. Abhayapala
Thushara D. Abhayapala Australian National University
Iven Mareels
Iven Mareels IBM (United States)

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