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.
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.
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.
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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Correcting Sample Selection Bias by Unlabeled Data
Jiayuan Huang;Arthur Gretton;Karsten M. Borgwardt;Bernhard Schölkopf.
neural information processing systems (2006)
Measuring statistical dependence with hilbert-schmidt norms
Arthur Gretton;Olivier Bousquet;Alex Smola;Bernhard Schölkopf.
algorithmic learning theory (2005)
A Kernel Method for the Two-Sample-Problem
Arthur Gretton;Karsten M. Borgwardt;Malte Rasch;Bernhard Schölkopf.
neural information processing systems (2006)
Integrating structured biological data by Kernel Maximum Mean Discrepancy
Karsten M. Borgwardt;Arthur Gretton;Malte J. Rasch;Hans-Peter Kriegel.
intelligent systems in molecular biology (2006)
Ranking on Data Manifolds
Dengyong Zhou;Jason Weston;Arthur Gretton;Olivier Bousquet.
neural information processing systems (2003)
A Hilbert space embedding for distributions
Alex Smola;Arthur Gretton;Le Song;Bernhard Schölkopf.
algorithmic learning theory (2007)
Correcting sample selection bias by unlabeled data
J Huang;AJ Smola;A Gretton;KM Borgwardt.
In: UNSPECIFIED (pp. 601-608). (2007) (2007)
A Kernel Statistical Test of Independence
Arthur Gretton;Kenji Fukumizu;Choon H. Teo;Le Song.
neural information processing systems (2007)
Hilbert Space Embeddings and Metrics on Probability Measures
Bharath K. Sriperumbudur;Arthur Gretton;Kenji Fukumizu;Bernhard Schölkopf.
Journal of Machine Learning Research (2010)
Covariate Shift by Kernel Mean Matching
A Gretton;AJ Smola;J Huang;M Schmittfull.
neural information processing systems (2009)
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