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Michael Arbel
Michael Arbel
Inria - Univ. Grenoble Alpes
Verified email at inria.fr - Homepage
Title
Cited by
Cited by
Year
Demystifying mmd gans
M Bińkowski, DJ Sutherland, M Arbel, A Gretton
International Conference on Learning Representations (ICLR) 2018, 2018
10392018
Maximum mean discrepancy gradient flow
M Arbel, A Korba, A Salim, A Gretton
Advances in Neural Information Processing Systems (NeurIPS) 2019, 2019
1142019
Generalized Energy Based Models
M Arbel, L Zhou, A Gretton
International Conference on Learning Representations (ICLR) 2021, 2021
1052021
On gradient regularizers for MMD GANs
M Arbel, DJ Sutherland, M Bińkowski, A Gretton
Advances in Neural Information Processing Systems (NeurIPS) 2018, 2018
992018
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
612020
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
372018
Annealed Flow Transport Monte Carlo
M Arbel, AGDG Matthews, A Doucet
International Conference on Machine Learning (ICML) 2021, 2021
352021
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
35*2021
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
292020
Amortized implicit differentiation for stochastic bilevel optimization
M Arbel, J Mairal
International Conference on Learning Representations (ICLR) 2022, 2022
252022
Kernel conditional exponential family
M Arbel, A Gretton
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2018
252018
Estimating barycenters of measures in high dimensions
S Cohen, M Arbel, MP Deisenroth
arXiv preprint arXiv:2007.07105, 2020
212020
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
192021
Kernelized Wasserstein Natural Gradient
M Arbel, A Gretton, W Li, G Montúfar
International Conference on Learning Representations (ICLR) 2020, 2020
182020
Non-Convex Bilevel Games with Critical Point Selection Maps
M Arbel, J Mairal
Advances in Neural Information Processing Systems (NeurIPS) 2022, 2022
152022
Efficient wasserstein natural gradients for reinforcement learning
T Moskovitz, M Arbel, F Huszar, A Gretton
International Conference on Learning Representations (ICLR) 2021, 2021
152021
Continual repeated annealed flow transport Monte Carlo
A Matthews, M Arbel, DJ Rezende, A Doucet
International Conference on Machine Learning, 15196-15219, 2022
142022
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
142021
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
92022
Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference
P Glaser, M Arbel, A Doucet, A Gretton
arXiv preprint arXiv:2210.14756, 2022
52022
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Articles 1–20