Obserwuj
Heiko Strathmann
Heiko Strathmann
Research Scientist at Deepmind
Zweryfikowany adres z deepmind.com - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
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
8452012
A kernel test of goodness of fit
K Chwialkowski, H Strathmann, A Gretton
International conference on machine learning, 2606-2615, 2016
3732016
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
2372016
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
1982018
On Russian roulette estimates for Bayesian inference with doubly-intractable likelihoods
AM Lyne, M Girolami, Y Atchadé, H Strathmann, D Simpson
159*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
1492021
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
1382016
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
942015
Learning deep kernels for exponential family densities
L Wenliang, DJ Sutherland, H Strathmann, A Gretton
International Conference on Machine Learning, 6737-6746, 2019
882019
Kernel adaptive metropolis-hastings
D Sejdinovic, H Strathmann, ML Garcia, C Andrieu, A Gretton
International conference on machine learning, 1665-1673, 2014
622014
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
412018
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
352022
Meta-learning mean functions for gaussian processes
V Fortuin, H Strathmann, G Rätsch
arXiv preprint arXiv:1901.08098, 2019
312019
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
262012
Neural variational gradient descent
LL di Langosco, V Fortuin, H Strathmann
arXiv preprint arXiv:2107.10731, 2021
212021
Sparse Gaussian processes on discrete domains
V Fortuin, G Dresdner, H Strathmann, G Rätsch
IEEE Access 9, 76750-76758, 2021
202021
Unbiased Bayes for big data: Paths of partial posteriors
H Strathmann, D Sejdinovic, M Girolami
arXiv preprint arXiv:1501.03326, 2015
192015
Persistent message passing
H Strathmann, M Barekatain, C Blundell, P Veličković
arXiv preprint arXiv:2103.01043, 2021
152021
Scaling instructable agents across many simulated worlds
MA Raad, A Ahuja, C Barros, F Besse, A Bolt, A Bolton, B Brownfield, ...
arXiv preprint arXiv:2404.10179, 2024
102024
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
92022
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