2026 Arthur Gretton: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	66	2264	2195	129	127	241	26680

Arthur Gretton publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Arthur Gretton sits on this spectrum.

32 publications 991+

This scientist: 241 publications — 60th percentile

60% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Arthur Gretton D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Arthur Gretton sits on this spectrum.

30 D-Index 131+

This scientist: 66 D-Index — 84th percentile

84% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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

3126
Citations
A Kernel Method for the Two-Sample-Problem

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

2177
Citations
Correcting Sample Selection Bias by Unlabeled Data

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

1947
Citations
Measuring statistical dependence with hilbert-schmidt norms

Arthur Gretton;Olivier Bousquet;Alex Smola;Bernhard Schölkopf

1789
Citations
Integrating structured biological data by Kernel Maximum Mean Discrepancy

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

1718
Citations
Ranking on Data Manifolds

Dengyong Zhou;Jason Weston;Arthur Gretton;Olivier Bousquet

934
Citations
A Hilbert space embedding for distributions

Alex Smola;Arthur Gretton;Le Song;Bernhard Schölkopf

910
Citations
A Kernel Statistical Test of Independence

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

789
Citations
Hilbert Space Embeddings and Metrics on Probability Measures

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

740
Citations
Correcting sample selection bias by unlabeled data

J Huang;AJ Smola;A Gretton;KM Borgwardt

679
Citations
Covariate Shift by Kernel Mean Matching

A Gretton;AJ Smola;J Huang;M Schmittfull

609
Citations
Optimal kernel choice for large-scale two-sample tests

Arthur Gretton;Dino Sejdinovic;Heiko Strathmann;Sivaraman Balakrishnan

600
Citations
Kernel Measures of Conditional Dependence

Kenji Fukumizu;Arthur Gretton;Xiaohai Sun;Bernhard Schölkopf

566
Citations
Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information.

Andrei Belitski;Arthur Gretton;Cesare Magri;Yusuke Murayama

500
Citations
Feature selection via dependence maximization

Le Song;Alex Smola;Arthur Gretton;Justin Bedo

428
Citations
Supervised feature selection via dependence estimation

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

426
Citations
Kernel Methods for Measuring Independence

Arthur Gretton;Ralf Herbrich;Alexander Smola;Olivier Bousquet

397
Citations
Demystifying MMD GANs

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

394
Citations
Statistical Consistency of Kernel Canonical Correlation Analysis

Kenji Fukumizu;Francis R. Bach;Arthur Gretton

321
Citations
On the empirical estimation of integral probability metrics

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

301
Citations
Inferring Spike Trains From Local Field Potentials

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

267
Citations
Kernel Embeddings of Conditional Distributions: A Unified Kernel Framework for Nonparametric Inference in Graphical Models

Le Song;K. Fukumizu;A. Gretton

243
Citations
Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions

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

221
Citations

Frequent Co-Authors

Kenji Fukumizu The Institute of Statistical Mathematics

Bernhard Schölkopf Max Planck Institute for Intelligent Systems

Alexander J. Smola Amazon (United States)

Le Song Mohamed bin Zayed University of Artificial Intelligence

Matthew B. Blaschko KU Leuven

Barnabás Póczos Carnegie Mellon University

Karsten M. Borgwardt Max Planck Institute of Biochemistry

Nikos K. Logothetis Chinese Academy of Sciences

Gert R. G. Lanckriet University of California, San Diego

Stefanie Jegelka MIT

External Links

Personal website of Arthur Gretton Google Scholar page

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