| Demystifying mmd gans M Bińkowski, DJ Sutherland, M Arbel, A Gretton International Conference on Learning Representations (ICLR) 2018, 2018 | 1484 | 2018 |
| Maximum mean discrepancy gradient flow M Arbel, A Korba, A Salim, A Gretton Advances in Neural Information Processing Systems (NeurIPS) 2019, 2019 | 156 | 2019 |
| Generalized Energy Based Models M Arbel, L Zhou, A Gretton International Conference on Learning Representations (ICLR) 2021, 2021 | 129 | 2021 |
| On gradient regularizers for MMD GANs M Arbel, DJ Sutherland, M Bińkowski, A Gretton Advances in Neural Information Processing Systems (NeurIPS) 2018, 2018 | 102 | 2018 |
| A non-asymptotic analysis for Stein variational gradient descent A Korba, A Salim, M Arbel, G Luise, A Gretton Advances in Neural Information Processing Systems (NeurIPS) 2020 33, 2020 | 93 | 2020 |
| Annealed Flow Transport Monte Carlo M Arbel, AGDG Matthews, A Doucet International Conference on Machine Learning (ICML) 2021, 2021 | 58 | 2021 |
| Tactical Optimism and Pessimism for Deep Reinforcement Learning T Moskovitz, J Parker-Holder, A Pacchiano, M Arbel, MI Jordan Advances in Neural Information Processing Systems (NeurIPS) 2021, 2021 | 53 | 2021 |
| Amortized implicit differentiation for stochastic bilevel optimization M Arbel, J Mairal International Conference on Learning Representations (ICLR) 2022, 2022 | 50 | 2022 |
| Efficient and principled score estimation with Nystr\" om kernel exponential families DJ Sutherland, H Strathmann, M Arbel, A Gretton International Conference on Artificial Intelligence and Statistics (AISTATS …, 2018 | 41 | 2018 |
| Synchronizing probability measures on rotations via optimal transport T Birdal, M Arbel, U Simsekli, LJ Guibas IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 1569 …, 2020 | 37 | 2020 |
| KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support P Glaser, M Arbel, A Gretton Advances in Neural Information Processing Systems (NeurIPS) 2021, 2021 | 30 | 2021 |
| Continual repeated annealed flow transport Monte Carlo A Matthews, M Arbel, DJ Rezende, A Doucet International Conference on Machine Learning, 15196-15219, 2022 | 27 | 2022 |
| Efficient wasserstein natural gradients for reinforcement learning T Moskovitz, M Arbel, F Huszar, A Gretton International Conference on Learning Representations (ICLR) 2021, 2021 | 26 | 2021 |
| Estimating barycenters of measures in high dimensions S Cohen, M Arbel, MP Deisenroth arXiv preprint arXiv:2007.07105, 2020 | 25 | 2020 |
| Kernel conditional exponential family M Arbel, A Gretton International Conference on Artificial Intelligence and Statistics (AISTATS …, 2018 | 25 | 2018 |
| Non-Convex Bilevel Games with Critical Point Selection Maps M Arbel, J Mairal Advances in Neural Information Processing Systems (NeurIPS) 2022, 2022 | 23 | 2022 |
| Kernelized Wasserstein Natural Gradient M Arbel, A Gretton, W Li, G Montúfar International Conference on Learning Representations (ICLR) 2020, 2020 | 22 | 2020 |
| The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods L Thiry, M Arbel, E Belilovsky, E Oyallon International Conference on Learning Representations (ICLR) 2021, 2021 | 15 | 2021 |
| Towards an Understanding of Default Policies in Multitask Policy Optimization T Moskovitz, M Arbel, J Parker-Holder, A Pacchiano International Conference on Artificial Intelligence and Statistics (AISTATS …, 2022 | 9 | 2022 |
| Maximum Likelihood Learning of Unnormalized Models for Simulation-Based Inference P Glaser, M Arbel, S Hromadka, A Doucet, A Gretton arXiv preprint arXiv:2210.14756, 2022 | 7* | 2022 |
Arthur GrettonGatsby Computational Neuroscience Unit and Google DeepmindVerified email at gatsby.ucl.ac.uk
