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Michael Arbel
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2020 – today
- 2024
- [j1]Juliette Marrie, Michael Arbel, Julien Mairal, Diane Larlus:
On Good Practices for Task-Specific Distillation of Large Pretrained Visual Models. Trans. Mach. Learn. Res. 2024 (2024) - [c21]Michael Arbel
, Alexandre Zouaoui
MLXP: A framework for conducting replicable experiments in Python. ACM-REP 2024 - [i24]Juliette Marrie, Michael Arbel, Julien Mairal, Diane Larlus:
On Good Practices for Task-Specific Distillation of Large Pretrained Models. CoRR abs/2402.11305 (2024) - [i23]Michael Arbel, Alexandre Zouaoui:
MLXP: A framework for conducting replicable Machine Learning eXperiments in Python. CoRR abs/2402.13831 (2024) - [i22]Ieva Petrulionyte, Julien Mairal, Michael Arbel:
Functional Bilevel Optimization for Machine Learning. CoRR abs/2403.20233 (2024) - [i21]Juliette Marrie, Romain Menegaux, Michael Arbel, Diane Larlus, Julien Mairal:
LUDVIG: Learning-free Uplifting of 2D Visual features to Gaussian Splatting scenes. CoRR abs/2410.14462 (2024) - 2023
- [c20]Juliette Marrie, Michael Arbel, Diane Larlus, Julien Mairal:
SLACK: Stable Learning of Augmentations with Cold-Start and KL Regularization. CVPR 2023: 24306-24314 - [c19]Michael Arbel, Romain Menegaux, Pierre Wolinski:
Rethinking Gauss-Newton for learning over-parameterized models. NeurIPS 2023 - [i20]Michael Arbel:
Rethinking Gauss-Newton for learning over-parameterized models. CoRR abs/2302.02904 (2023) - [i19]Juliette Marrie, Michael Arbel, Diane Larlus, Julien Mairal:
SLACK: Stable Learning of Augmentations with Cold-start and KL regularization. CoRR abs/2306.09998 (2023) - 2022
- [c18]Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano:
Towards an Understanding of Default Policies in Multitask Policy Optimization. AISTATS 2022: 10661-10686 - [c17]Michael Arbel, Julien Mairal:
Amortized Implicit Differentiation for Stochastic Bilevel Optimization. ICLR 2022 - [c16]Alexander G. de G. Matthews, Michael Arbel, Danilo Jimenez Rezende, Arnaud Doucet:
Continual Repeated Annealed Flow Transport Monte Carlo. ICML 2022: 15196-15219 - [c15]Michael Arbel, Julien Mairal:
Non-Convex Bilevel Games with Critical Point Selection Maps. NeurIPS 2022 - [i18]Alexander G. de G. Matthews, Michael Arbel, Danilo J. Rezende, Arnaud Doucet:
Continual Repeated Annealed Flow Transport Monte Carlo. CoRR abs/2201.13117 (2022) - [i17]Pierre Glaser, Michael Arbel, Arnaud Doucet, Arthur Gretton:
Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference. CoRR abs/2210.14756 (2022) - 2021
- [c14]Michael Arbel, Liang Zhou, Arthur Gretton:
Generalized Energy Based Models. ICLR 2021 - [c13]Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton:
Efficient Wasserstein Natural Gradients for Reinforcement Learning. ICLR 2021 - [c12]Louis Thiry, Michael Arbel, Eugene Belilovsky, Edouard Oyallon:
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods. ICLR 2021 - [c11]Michael Arbel, Alexander G. de G. Matthews, Arnaud Doucet:
Annealed Flow Transport Monte Carlo. ICML 2021: 318-330 - [c10]Pierre Glaser, Michael Arbel, Arthur Gretton:
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support. NeurIPS 2021: 8018-8031 - [c9]Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan:
Tactical Optimism and Pessimism for Deep Reinforcement Learning. NeurIPS 2021: 12849-12863 - [i16]Louis Thiry, Michael Arbel, Eugene Belilovsky, Edouard Oyallon:
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods. CoRR abs/2101.07528 (2021) - [i15]Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel:
Deep Reinforcement Learning with Dynamic Optimism. CoRR abs/2102.03765 (2021) - [i14]Michael Arbel, Alexander G. de G. Matthews, Arnaud Doucet:
Annealed Flow Transport Monte Carlo. CoRR abs/2102.07501 (2021) - [i13]Pierre Glaser, Michael Arbel, Arthur Gretton:
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support. CoRR abs/2106.08929 (2021) - [i12]Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano:
Towards an Understanding of Default Policies in Multitask Policy Optimization. CoRR abs/2111.02994 (2021) - [i11]Michael Arbel, Julien Mairal:
Amortized Implicit Differentiation for Stochastic Bilevel Optimization. CoRR abs/2111.14580 (2021) - 2020
- [c8]Tolga Birdal
Synchronizing Probability Measures on Rotations via Optimal Transport. CVPR 2020: 1566-1576 - [c7]Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montúfar:
Kernelized Wasserstein Natural Gradient. ICLR 2020 - [c6]Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton:
A Non-Asymptotic Analysis for Stein Variational Gradient Descent. NeurIPS 2020 - [i10]Michael Arbel, Liang Zhou, Arthur Gretton:
KALE: When Energy-Based Learning Meets Adversarial Training. CoRR abs/2003.05033 (2020) - [i9]Tolga Birdal, Michael Arbel, Umut Simsekli, Leonidas J. Guibas:
Synchronizing Probability Measures on Rotations via Optimal Transport. CoRR abs/2004.00663 (2020) - [i8]Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton:
A Non-Asymptotic Analysis for Stein Variational Gradient Descent. CoRR abs/2006.09797 (2020) - [i7]Samuel Cohen, Michael Arbel, Marc Peter Deisenroth:
Estimating Barycenters of Measures in High Dimensions. CoRR abs/2007.07105 (2020) - [i6]Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton:
Efficient Wasserstein Natural Gradients for Reinforcement Learning. CoRR abs/2010.05380 (2020)
2010 – 2019
- 2019
- [c5]Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton:
Maximum Mean Discrepancy Gradient Flow. NeurIPS 2019: 6481-6491 - [i5]Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton:
Maximum Mean Discrepancy Gradient Flow. CoRR abs/1906.04370 (2019) - [i4]Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montúfar
Kernelized Wasserstein Natural Gradient. CoRR abs/1910.09652 (2019) - 2018
- [c4]Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton:
Efficient and principled score estimation with Nyström kernel exponential families. AISTATS 2018: 652-660 - [c3]Michael Arbel, Arthur Gretton:
Kernel Conditional Exponential Family. AISTATS 2018: 1337-1346 - [c2]Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton:
Demystifying MMD GANs. ICLR (Poster) 2018 - [c1]Michael Arbel, Danica J. Sutherland, Mikolaj Binkowski, Arthur Gretton:
On gradient regularizers for MMD GANs. NeurIPS 2018: 6701-6711 - [i3]Mikolaj Binkowski, Danica J. Sutherland
Demystifying MMD GANs. CoRR abs/1801.01401 (2018) - [i2]Michael Arbel, Danica J. Sutherland
On gradient regularizers for MMD GANs. CoRR abs/1805.11565 (2018) - 2017
- [i1]Danica J. Sutherland
Efficient and principled score estimation. CoRR abs/1705.08360 (2017)
Coauthor Index
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last updated on 2024-11-28 21:28 CET by the dblp team
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