What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
His scientific interests lie mostly in Kernel, Reproducing kernel Hilbert space, Kernel method, Applied mathematics and Statistics.
His specific area of interest is Kernel, where Arthur Gretton studies Kernel embedding of distributions.
His Reproducing kernel Hilbert space study combines topics from a wide range of disciplines, such as Probability distribution and Probability measure.
His Kernel method research is multidisciplinary, relying on both Independence, Key and Feature selection.
Asymptotic distribution, Null distribution, Kolmogorov–Smirnov test, Random variable and Brown–Forsythe test is closely connected to Statistic in his research, which is encompassed under the umbrella topic of Applied mathematics.
The various areas that Arthur Gretton examines in his Statistics study include Connection and Covariate shift.
His most cited work include:
- A kernel two-sample test (1931 citations)
- Correcting Sample Selection Bias by Unlabeled Data (972 citations)
- A Kernel Method for the Two-Sample-Problem (919 citations)
What are the main themes of his work throughout his whole career to date?
Arthur Gretton focuses on Kernel, Reproducing kernel Hilbert space, Algorithm, Artificial intelligence and Applied mathematics.
His research in Kernel intersects with topics in Nonparametric statistics and Statistical hypothesis testing.
His Reproducing kernel Hilbert space research incorporates elements of Probability distribution, Embedding, Estimator, Density estimation and Probability measure.
His Algorithm study integrates concerns from other disciplines, such as Data mining, Measure, Sampling, Kernel and Entropy.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Pattern recognition.
His work focuses on many connections between Applied mathematics and other disciplines, such as Distribution, that overlap with his field of interest in Analytic function.
He most often published in these fields:
- Kernel (49.46%)
- Reproducing kernel Hilbert space (32.62%)
- Algorithm (28.32%)
What were the highlights of his more recent work (between 2018-2021)?
- Kernel (49.46%)
- Applied mathematics (20.43%)
- Reproducing kernel Hilbert space (32.62%)
In recent papers he was focusing on the following fields of study:
Kernel, Applied mathematics, Reproducing kernel Hilbert space, Nonparametric statistics and Estimator are his primary areas of study.
The Kernel study combines topics in areas such as Test, Feature, Simple, Asymptotic analysis and Generalization.
Arthur Gretton combines subjects such as Flow, Probability distribution, Balanced flow and Metric with his study of Applied mathematics.
His Reproducing kernel Hilbert space research incorporates themes from Covariate and Probability measure.
Arthur Gretton has researched Nonparametric statistics in several fields, including Statistical hypothesis testing, Kernel and Consistency.
His studies in Estimator integrate themes in fields like Embedding, Kernel method and Algorithm.
Between 2018 and 2021, his most popular works were:
- Learning deep kernels for exponential family densities (28 citations)
- Maximum Mean Discrepancy Gradient Flow (26 citations)
- Kernel Instrumental Variable Regression (23 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Artificial intelligence
Arthur Gretton mainly focuses on Algorithm, Reproducing kernel Hilbert space, Artificial neural network, Applied mathematics and Nonparametric statistics.
He interconnects Exponential family, Distribution and Function in the investigation of issues within Algorithm.
Reproducing kernel Hilbert space is a subfield of Kernel that he tackles.
His research in Kernel focuses on subjects like Matching, which are connected to Smoothness.
Arthur Gretton has included themes like Flow, Metric, Balanced flow and Probability measure in his Applied mathematics study.
The concepts of his Nonparametric statistics study are interwoven with issues in Statistical hypothesis testing, Instrumental variable, Minimax, Multiple kernel learning and Kernel.
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