Heiko Strathmann - Concepedia

Author

Heiko Strathmann

University College London ORCID

Also Known As

Heiko Strathmann, Heiko Strathmann, Soyer, Hubert, Strathmann, Heiko

Publications

1.4K

Citations

H-Index

Concepts

All Affiliations

University College London Max Planck Institute for Intelligent Systems Max Planck Society University of Warwick Google DeepMind (United Kingdom)

About

Heiko Strathmann is an author at University College London specializing in engineering, machine learning, and data science.

Top Concepts

#	Concept	H-Index	Publications	Citations
1	Engineering	12	13	532
2	Natural Sciences	2	2	51
3	Biostatistics	2	2	114
4	Medicine	1	1	23
5	Public Health	1	1	102

Heiko Strathmann

Publications

1.3K

Citations

H-Index

Page 1

	Year	Citations
Optimal kernel choice for large-scale two-sample tests Arthur Gretton, Dino Sejdinović, Heiko Strathmann,	2012	505
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy Danica J. Sutherland, Hsiao-Yu Fish Tung, Heiko Strathmann, arXiv (Cornell University) Artificial IntelligenceEngineeringMachine LearningGenerative StudyGenerative System	2016	108
31st International Conference on Machine Learning, ICML 2014 Dino Sejdinović, Heiko Strathmann, Maria Lomeli Garcia, International Conference on Machine Learning Artificial IntelligenceEngineeringMachine LearningComputational Learning TheoryPattern Recognition	2014	108
A Kernel Test of Goodness of Fit Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton arXiv (Cornell University) Log GradientsDensity EstimationBootstrap ResamplingNonparametric Statistical TestStatistical Foundation	2016	102
On Russian Roulette Estimates for Bayesian Inference with Doubly-Intractable Likelihoods Anne-Marie Lyne, Mark Girolami, Yves F. Atchadé, Statistical Science	2015	99
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy Danica J. Sutherland, Hsiao-Yu Fish Tung, Heiko Strathmann, arXiv (Cornell University) Artificial IntelligenceEngineeringMachine LearningGenerative StudyGenerative System	2016	73
SOM-VAE: Interpretable Discrete Representation Learning on Time Series Vincent Fortuin, Matthias Hüser, Francesco Locatello, arXiv (Cornell University) EngineeringMachine LearningData ScienceEntropyKnowledge Discovery	2018	60
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families Heiko Strathmann, Dino Sejdinović, Samuel Livingstone, arXiv (Cornell University) Hamiltonian TheoryHamiltonian Monte CarloEngineeringMachine LearningData Science	2015	33
SOM-VAE: Interpretable Discrete Representation Learning on Time Series Vincent Fortuin, Matthias Hüser, Francesco Locatello, arXiv (Cornell University) EngineeringMachine LearningData ScienceEntropyKnowledge Discovery	2018	28
Kernel Adaptive Metropolis-Hastings Dino Sejdinović, Heiko Strathmann, Maria Lomeli Garcia, arXiv (Cornell University)	2013	26

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