| 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 25, 2012 | 802 | 2012 |
| A kernel test of goodness of fit K Chwialkowski, H Strathmann, A Gretton International conference on machine learning, 2606-2615, 2016 | 353 | 2016 |
| 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 | 219 | 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 | 185 | 2018 |
| On Russian roulette estimates for Bayesian inference with doubly-intractable likelihoods AM Lyne, M Girolami, Y Atchadé, H Strathmann, D Simpson | 156* | 2015 |
| Nerf-vae: A geometry aware 3d scene generative model AR Kosiorek, H Strathmann, D Zoran, P Moreno, R Schneider, S Mokrá, ... International Conference on Machine Learning, 5742-5752, 2021 | 132 | 2021 |
| 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 2, 2016 | 130 | 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 28, 2015 | 94 | 2015 |
| Learning deep kernels for exponential family densities L Wenliang, DJ Sutherland, H Strathmann, A Gretton International Conference on Machine Learning, 6737-6746, 2019 | 81 | 2019 |
| Kernel adaptive metropolis-hastings D Sejdinovic, H Strathmann, ML Garcia, C Andrieu, A Gretton International conference on machine learning, 1665-1673, 2014 | 61 | 2014 |
| Efficient and principled score estimation with nyström kernel exponential families DJ Sutherland, H Strathmann, M Arbel, A Gretton International Conference on Artificial Intelligence and Statistics, 652-660, 2018 | 41 | 2018 |
| Meta-learning mean functions for gaussian processes V Fortuin, H Strathmann, G Rätsch arXiv preprint arXiv:1901.08098, 2019 | 30 | 2019 |
| Score-based diffusion meets annealed importance sampling A Doucet, W Grathwohl, AG Matthews, H Strathmann Advances in Neural Information Processing Systems 35, 21482-21494, 2022 | 28 | 2022 |
| 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 | 26 | 2012 |
| Sparse Gaussian processes on discrete domains V Fortuin, G Dresdner, H Strathmann, G Rätsch IEEE Access 9, 76750-76758, 2021 | 19 | 2021 |
| Unbiased Bayes for big data: Paths of partial posteriors H Strathmann, D Sejdinovic, M Girolami arXiv preprint arXiv:1501.03326, 2015 | 19 | 2015 |
| Neural variational gradient descent LL di Langosco, V Fortuin, H Strathmann arXiv preprint arXiv:2107.10731, 2021 | 17 | 2021 |
| Persistent message passing H Strathmann, M Barekatain, C Blundell, P Veličković arXiv preprint arXiv:2103.01043, 2021 | 15 | 2021 |
| Kernel sequential Monte Carlo I Schuster, H Strathmann, B Paige, D Sejdinovic Machine Learning and Knowledge Discovery in Databases: European Conference …, 2017 | 9 | 2017 |
| Annealed importance sampling meets score matching A Doucet, WS Grathwohl, AGG Matthews, H Strathmann ICLR Workshop on Deep Generative Models for Highly Structured Data, 2022 | 8 | 2022 |
Arthur GrettonGatsby Computational Neuroscience Unit and Google DeepmindVerified email at gatsby.ucl.ac.uk
