Michael Arbel
Michael Arbel
Gatsby Computational Neuroscience Unit, UCL
Verified email at ucl.ac.uk - Homepage
Title
Cited by
Cited by
Year
Demystifying mmd gans
M Bińkowski, DJ Sutherland, M Arbel, A Gretton
International Conference on Learning Representations, 2018
2532018
On gradient regularizers for MMD GANs
M Arbel, DJ Sutherland, M Bińkowski, A Gretton
arXiv preprint arXiv:1805.11565, 2018
592018
Maximum mean discrepancy gradient flow
M Arbel, A Korba, A Salim, A Gretton
arXiv preprint arXiv:1906.04370, 2019
222019
Efficient and principled score estimation with Nystr\" om kernel exponential families
DJ Sutherland, H Strathmann, M Arbel, A Gretton
Proceedings of the Twenty-First International Conference on Artificial …, 2018
21*2018
Kernel conditional exponential family
M Arbel, A Gretton
Proceedings of the Twenty-First International Conference on Artificial …, 2018
142018
Synchronizing probability measures on rotations via optimal transport
T Birdal, M Arbel, U Simsekli, LJ Guibas
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
72020
Kernelized Wasserstein Natural Gradient
M Arbel, A Gretton, W Li, G Montúfar
arXiv preprint arXiv:1910.09652, 2019
62019
Generalized Energy Based Models
M Arbel, L Zhou, A Gretton
arXiv preprint arXiv:2003.05033, 2020
5*2020
Estimating Barycenters of Measures in High Dimensions
S Cohen, M Arbel, MP Deisenroth
arXiv preprint arXiv:2007.07105, 2020
32020
A non-asymptotic analysis for Stein variational gradient descent
A Korba, A Salim, M Arbel, G Luise, A Gretton
arXiv preprint arXiv:2006.09797, 2020
22020
Efficient Wasserstein Natural Gradients for Reinforcement Learning
T Moskovitz, M Arbel, F Huszar, A Gretton
arXiv preprint arXiv:2010.05380, 2020
12020
Annealed Flow Transport Monte Carlo
M Arbel, AGDG Matthews, A Doucet
arXiv preprint arXiv:2102.07501, 2021
2021
Deep Reinforcement Learning with Dynamic Optimism
T Moskovitz, J Parker-Holder, A Pacchiano, M Arbel
arXiv preprint arXiv:2102.03765, 2021
2021
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods
L Thiry, M Arbel, E Belilovsky, E Oyallon
arXiv preprint arXiv:2101.07528, 2021
2021
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Articles 1–14