| 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 | 315 | 2012 |
| A kernel test of goodness of fit K Chwialkowski, H Strathmann, A Gretton JMLR: Workshop and Conference Proceedings 48, 2606-2615, 2016 | 147 | 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 | 86 | 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 | 75 | 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 | 63 | 2015 |
| 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 | 50 | 2016 |
| 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 | 43 | 2018 |
| Kernel adaptive metropolis-hastings D Sejdinovic, H Strathmann, ML Garcia, C Andrieu, A Gretton International conference on machine learning, 1665-1673, 2014 | 40 | 2014 |
| Learning deep kernels for exponential family densities L Wenliang, D Sutherland, H Strathmann, A Gretton International Conference on Machine Learning, 6737-6746, 2019 | 24 | 2019 |
| 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 | 22 | 2012 |
| Unbiased Bayes for big data: Paths of partial posteriors H Strathmann, D Sejdinovic, M Girolami arXiv preprint arXiv:1501.03326, 2015 | 19 | 2015 |
| Playing Russian roulette with intractable likelihoods M Girolami, AM Lyne, H Strathmann, D Simpson, Y Atchade arXiv preprint arXiv:1306.4032, 2013 | 17 | 2013 |
| Efficient and principled score estimation with nyström kernel exponential families D Sutherland, H Strathmann, M Arbel, A Gretton International Conference on Artificial Intelligence and Statistics, 652-660, 2018 | 15 | 2018 |
| Scalable gaussian processes on discrete domains V Fortuin, G Dresdner, H Strathmann, G Rätsch arXiv preprint arXiv:1810.10368, 2018 | 7 | 2018 |
| Kernel Sequential Monte Carlo I Schuster, H Strathmann, B Paige, D Sejdinovic Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017 | 4 | 2017 |
| Efficient and principled score estimation DJ Sutherland, H Strathmann, M Arbel, A Gretton arXiv preprint arXiv:1705.08360, 2017 | 3 | 2017 |
| Adaptive Large-Scale Kernel Two-Sample Testing H Strathmann M. Sc. University College London.: http://herrstrathmann. de/wp-content …, 2012 | 2 | 2012 |
| Kernel methods for Monte Carlo H Strathmann UCL (University College London), 2018 | 1 | 2018 |
| Efficient and principled score estimation with Nyström kernel exponential families DJ Sutherland, H Strathmann, M Arbel, A Gretton arXiv, arXiv: 1705.08360, 2017 | 1 | 2017 |
| 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 | 1 | 2017 |
arthur grettonGatsby Computational Neuroscience Unit, UCLVerified email at gatsby.ucl.ac.uk
