World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
12268
World Ranking
11885
National Ranking
362

Overview

Richard Nock is affiliated with the Australian National University in Australia and primarily works within the field of computer science. Their research extensively covers artificial intelligence, computer vision and pattern recognition, cognitive neuroscience, statistics and probability, as well as statistical and nonlinear physics.

Their scholarly output includes a significant focus on topics such as machine learning and data classification, machine learning and algorithms, adversarial robustness in machine learning, domain adaptation and few-shot learning, generative adversarial networks and image synthesis, explainable artificial intelligence (XAI), and human pose and action recognition.

Some of the recent publications attributed to Richard Nock include:

  • Advances and Open Problems in Federated Learning (2020), published in Foundations and Trends® in Machine Learning
  • Adversarial Vulnerabilities of Human Decision-Making (2020), published in Proceedings of the National Academy of Sciences
  • Manifold Learning Benefits GANs (2022), presented at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • SMINT (2020), published in ACM Transactions on the Web
  • Generalised Lipschitz Regularisation Equals Distributional Robustness (2020), published on arXiv (Cornell University)

Frequent co-authors include:

  • Frank Nielsen
  • Ehsan Amid
  • Manfred K. Warmuth
  • Sanmi Koyejo
  • Piotr Koniusz

Richard Nock's research has been disseminated through various publication venues, with multiple papers appearing in:

  • arXiv (Cornell University)
  • Proceedings of the National Academy of Sciences
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Foundations and Trends® in Machine Learning
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Best Publications

  • Advances and Open Problems in Federated Learning

    Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet

  • Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach

    Giorgio Patrini;Giorgio Patrini;Alessandro Rozza;Aditya Krishna Menon;Aditya Krishna Menon;Richard Nock;Richard Nock;Richard Nock

  • Advances and open problems in federated learning

    Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet

  • Statistical region merging

    R. Nock;F. Nielsen

  • Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption.

    Stephen Hardy;Wilko Henecka;Hamish Ivey-Law;Richard Nock

  • Adaptive Subspaces for Few-Shot Learning

    Christian Simon;Piotr Koniusz;Richard Nock;Mehrtash Harandi

  • On weighting clustering

    R. Nock;F. Nielsen

  • Sided and Symmetrized Bregman Centroids

    F. Nielsen;R. Nock

  • A hybrid filter/wrapper approach of feature selection using information theory

    Marc Sebban;Richard Nock

  • Bregman Voronoi Diagrams

    Jean-Daniel Boissonnat;Frank Nielsen;Richard Nock

  • Learning and evaluation in the presence of class hierarchies: application to text categorization

    Svetlana Kiritchenko;Stan Matwin;Richard Nock;A. Fazel Famili

  • Bregman Voronoi Diagrams: Properties, Algorithms and Applications

    Jean-Daniel Boissonnat;Frank Nielsen;Richard Nock

  • A closed-form expression for the Sharma-Mittal entropy of exponential families

    Frank Nielsen;Richard Nock

  • On Bregman Voronoi diagrams

    Frank Nielsen;Jean-Daniel Boissonnat;Richard Nock

  • Almost) No Label No Cry

    Giorgio Patrini;Richard Nock;Tiberio Caetano;Paul Rivera

  • Entropies and cross-entropies of exponential families

    Frank Nielsen;Richard Nock

  • Entity Resolution and Federated Learning get a Federated Resolution.

    Richard Nock;Stephen Hardy;Wilko Henecka;Hamish Ivey-Law

  • Bregman Divergences and Surrogates for Learning

    R. Nock;F. Nielsen

  • Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach

    Giorgio Patrini;Giorgio Patrini;Alessandro Rozza;Aditya Menon;Aditya Menon;Richard Nock;Richard Nock;Richard Nock

  • Fitting the smallest enclosing bregman ball

    Richard Nock;Frank Nielsen

  • Loss factorization, weakly supervised learning and label noise robustness

    Giorgio Patrini;Frank Nielsen;Richard Nock;Marcello Carioni

  • Impact of learning set quality and size on decision tree performances.

    Marc Sebban;Richard Nock;Jean-Hugues Chauchat;Ricco Rakotomalala

  • A Real generalization of discrete AdaBoost

    Richard Nock;Frank Nielsen

Frequent Co-Authors

Frank Nielsen
Frank Nielsen Sony Computer Science Laboratories
Aditya Krishna Menon
Aditya Krishna Menon Google (United States)
Michel Barlaud
Michel Barlaud Université Côte d'Azur
Robert C. Williamson
Robert C. Williamson University of Tübingen
Peter Dayan
Peter Dayan Max Planck Institute for Biological Cybernetics
Jean-Daniel Boissonnat
Jean-Daniel Boissonnat Université Côte d'Azur
Mehrtash Harandi
Mehrtash Harandi Monash University
Shun-ichi Amari
Shun-ichi Amari RIKEN Center for Brain Science
Dawn Song
Dawn Song University of California, Berkeley

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