Heiko Strathmann
Heiko Strathmann
Alan Turing Institute, shogun.ml
Verified email at ucl.ac.uk - Homepage
TitleCited byYear
Optimal kernel choice for large-scale two-sample tests
A Gretton, D Sejdinovic, H Strathmann, S Balakrishnan, M Pontil, ...
Advances in neural information processing systems, 1205-1213, 2012
A kernel test of goodness of fit
K Chwialkowski, H Strathmann, A Gretton
JMLR: Workshop and Conference Proceedings 48, 2606-2615, 2016
On Russian roulette estimates for Bayesian inference with doubly-intractable likelihoods
AM Lyne, M Girolami, Y Atchadé, H Strathmann, D Simpson
Statistical science 30 (4), 443-467, 2015
Generative models and model criticism via optimized maximum mean discrepancy
DJ Sutherland, HY Tung, H Strathmann, S De, A Ramdas, A Smola, ...
arXiv preprint arXiv:1611.04488, 2016
Gradient-free Hamiltonian Monte Carlo with efficient kernel exponential families
H Strathmann, D Sejdinovic, S Livingstone, Z Szabo, A Gretton
Advances in Neural Information Processing Systems, 955-963, 2015
Kernel adaptive metropolis-hastings
D Sejdinovic, H Strathmann, ML Garcia, C Andrieu, A Gretton
International Conference on Machine Learning, 1665-1673, 2014
Soumyajit De, Aaditya Ramdas, Alex Smola, and Arthur Gretton. Generative models and model criticism via optimized maximum mean discrepancy
DJ Sutherland, HY Tung, H Strathmann
arXiv preprint arXiv:1611.04488, 2016
Escape from a dominant HLA-B* 15-restricted CD8+ T cell response against hepatitis C virus requires compensatory mutations outside the epitope
M Ruhl, P Chhatwal, H Strathmann, T Kuntzen, D Bankwitz, K Skibbe, ...
Journal of virology 86 (2), 991-1000, 2012
Playing Russian roulette with intractable likelihoods
M Girolami, AM Lyne, H Strathmann, D Simpson, Y Atchade
arXiv preprint arXiv:1306.4032, 2013
Unbiased Bayes for big data: Paths of partial posteriors
H Strathmann, D Sejdinovic, M Girolami
arXiv preprint arXiv:1501.03326, 2015
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
V Fortuin, M Hüser, F Locatello, H Strathmann, G Rätsch
arXiv preprint arXiv:1806.02199, 2018
Efficient and principled score estimation with Nystr\" om kernel exponential families
DJ Sutherland, H Strathmann, M Arbel, A Gretton
arXiv preprint arXiv:1705.08360, 2017
Learning deep kernels for exponential family densities
L Wenliang, D Sutherland, H Strathmann, A Gretton
arXiv preprint arXiv:1811.08357, 2018
Scalable Gaussian Processes on Discrete Domains
V Fortuin, G Dresdner, H Strathmann, G Rätsch
arXiv preprint arXiv:1810.10368, 2018
Kernel Sequential Monte Carlo
I Schuster, H Strathmann, B Paige, D Sejdinovic
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017
Efficient and principled score estimation
DJ Sutherland, H Strathmann, M Arbel, A Gretton
stat 1050, 19, 2017
Adaptive Large-Scale Kernel Two-Sample Testing
H Strathmann
M. Sc. University College London.: http://herrstrathmann. de/wp-content …, 2012
Kernel methods for Monte Carlo
H Strathmann
UCL (University College London), 2018
A determinant‐free method to simulate the parameters of large Gaussian fields
L Ellam, H Strathmann, M Girolami, I Murray
Stat 6 (1), 271-281, 2017
Kernel techniques for adaptive Monte Carlo methods
H Strathmann, D Sejdinovic, S Livingston, I Schuster, M Lomeli Garcia, ...
Greek Stochastics Workshop on Big Data and Big Models, 2016
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