World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
66
Citations
26680
World Ranking
2264
National Ranking
129

Overview

Arthur Gretton is affiliated with University College London in the United Kingdom. Their work spans multiple fields within computer science and mathematics, with a strong emphasis on artificial intelligence and statistics.

Gretton's research focuses notably on statistical methods and inference, advanced causal inference techniques, domain adaptation and few-shot learning, Bayesian modeling and causal inference, neural networks applications, and Gaussian processes.

Key research topics include:

  • Statistical Methods and Inference
  • Advanced Causal Inference Techniques
  • Domain Adaptation and Few-Shot Learning
  • Bayesian Modeling and Causal Inference
  • Statistical Methods and Bayesian Inference
  • Neural Networks and Applications
  • Gaussian Processes and Bayesian Inference

Their recent publications showcase work on kernel methods and computational statistics, with papers addressing both theoretical and practical aspects of machine learning and statistical analysis. Selected recent papers include:

  • Learning Deep Kernels for Non-Parametric Two-Sample Tests (2020), arXiv (Cornell University)
  • Stein's Method Meets Computational Statistics: A Review of Some Recent Developments (2022), Statistical Science
  • Self-Supervised Learning with Kernel Dependence Maximization (2021), arXiv (Cornell University)
  • A case for new neural network smoothness constraints (2020), arXiv (Cornell University)
  • A Non-Asymptotic Analysis for Stein Variational Gradient Descent (2020), arXiv (Cornell University)

Arthur Gretton frequently publishes in venues that include:

  • arXiv (Cornell University)
  • Biometrika
  • Statistical Science
  • Journal of the American Statistical Association
  • Journal of the Royal Statistical Society Series B (Statistical Methodology)

Their co-authorship network comprises several frequent collaborators, reflecting joint work on methods related to machine learning and statistics. Some of the most frequent co-authors are:

  • Dimitri Meunier
  • Antonin Schrab
  • Arnaud Doucet
  • Liyuan Xu
  • Michael Arbel

Their contributions span 89 publications in computer science and 58 in mathematics, highlighting a multidisciplinary approach to research challenges at the intersection of these fields.

Best Publications

  • A kernel two-sample test

    Arthur Gretton;Karsten M. Borgwardt;Malte J. Rasch;Bernhard Schölkopf

  • A Kernel Method for the Two-Sample-Problem

    Arthur Gretton;Karsten M. Borgwardt;Malte Rasch;Bernhard Schölkopf

  • Correcting Sample Selection Bias by Unlabeled Data

    Jiayuan Huang;Arthur Gretton;Karsten M. Borgwardt;Bernhard Schölkopf

  • Measuring statistical dependence with hilbert-schmidt norms

    Arthur Gretton;Olivier Bousquet;Alex Smola;Bernhard Schölkopf

  • Integrating structured biological data by Kernel Maximum Mean Discrepancy

    Karsten M. Borgwardt;Arthur Gretton;Malte J. Rasch;Hans-Peter Kriegel

  • Ranking on Data Manifolds

    Dengyong Zhou;Jason Weston;Arthur Gretton;Olivier Bousquet

  • A Hilbert space embedding for distributions

    Alex Smola;Arthur Gretton;Le Song;Bernhard Schölkopf

  • A Kernel Statistical Test of Independence

    Arthur Gretton;Kenji Fukumizu;Choon H. Teo;Le Song

  • Hilbert Space Embeddings and Metrics on Probability Measures

    Bharath K. Sriperumbudur;Arthur Gretton;Kenji Fukumizu;Bernhard Schölkopf

  • Correcting sample selection bias by unlabeled data

    J Huang;AJ Smola;A Gretton;KM Borgwardt

  • Covariate Shift by Kernel Mean Matching

    A Gretton;AJ Smola;J Huang;M Schmittfull

  • Optimal kernel choice for large-scale two-sample tests

    Arthur Gretton;Dino Sejdinovic;Heiko Strathmann;Sivaraman Balakrishnan

  • Kernel Measures of Conditional Dependence

    Kenji Fukumizu;Arthur Gretton;Xiaohai Sun;Bernhard Schölkopf

  • Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information.

    Andrei Belitski;Arthur Gretton;Cesare Magri;Yusuke Murayama

  • Feature selection via dependence maximization

    Le Song;Alex Smola;Arthur Gretton;Justin Bedo

  • Supervised feature selection via dependence estimation

    Le Song;Alex Smola;Arthur Gretton;Karsten M. Borgwardt

  • Kernel Methods for Measuring Independence

    Arthur Gretton;Ralf Herbrich;Alexander Smola;Olivier Bousquet

  • Demystifying MMD GANs

    Mikolaj Binkowski;Danica J. Sutherland;Michael Arbel;Arthur Gretton

  • Statistical Consistency of Kernel Canonical Correlation Analysis

    Kenji Fukumizu;Francis R. Bach;Arthur Gretton

  • On the empirical estimation of integral probability metrics

    Bharath K. Sriperumbudur;Kenji Fukumizu;Arthur Gretton;Bernhard Schoelkopf

  • Inferring Spike Trains From Local Field Potentials

    Malte J. Rasch;Arthur Gretton;Yusuke Murayama;Wolfgang Maass

  • Kernel Embeddings of Conditional Distributions: A Unified Kernel Framework for Nonparametric Inference in Graphical Models

    Le Song;K. Fukumizu;A. Gretton

  • Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions

    Kenji Fukumizu;Arthur Gretton;Gert R. Lanckriet;Bernhard Schölkopf

Frequent Co-Authors

Kenji Fukumizu
Kenji Fukumizu The Institute of Statistical Mathematics
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Le Song
Le Song Mohamed bin Zayed University of Artificial Intelligence
Barnabás Póczos
Barnabás Póczos Carnegie Mellon University
Karsten M. Borgwardt
Karsten M. Borgwardt Max Planck Institute of Biochemistry
Nikos K. Logothetis
Nikos K. Logothetis Chinese Academy of Sciences
Gert R. G. Lanckriet
Gert R. G. Lanckriet University of California, San Diego

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science in the USA opens many doors. Many students are now looking for flexible and budget-friendly options. For data science hopefuls, pursuing an affordable data science degree can provide high-value skills without high tuition costs.

If your interests include hardware and advanced systems, consider enrolling in one of the top online electrical engineering schools. These programs make it possible to learn at your own pace, wherever you are.

Short on time, but still want impressive credentials? There are quick certifications that pay well, letting you boost employability quickly in IT, cybersecurity, and programming.

For those seeking advanced credentials without a long school commitment, the fastest masters degree online options can help you enter high-demand fields like artificial intelligence and analytics in under two years.

There’s never been a better time to take charge of your tech career—online degrees and certifications are making it more flexible and affordable than ever before.

Best Scientists Citing Arthur Gretton

Trending Scientists

Recently Published Articles