Michael Arbel - RankMe

54 papers A* 17A 3Journal 32Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Non-Stationary Functional Bilevel Optimization. CoRR Jason Bohne, Ieva Petrulionyte, Michael Arbel, Julien Mairal, Pawel Polak
2025	J	`jnl`	EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks. CoRR Michael Arbel, David Salinas, Frank Hutter
2025	J	`jnl`	Flow Matching for Robust Simulation-Based Inference under Model Misspecification. CoRR Pierre-Louis Ruhlmann, Pedro L. C. Rodrigues, Michael Arbel, Florence Forbes
2025	J	`jnl`	Learning Theory for Kernel Bilevel Optimization. CoRR Fares El Khoury, Edouard Pauwels, Samuel Vaiter, Michael Arbel
2025	J	`jnl`	MAP Estimation with Denoisers: Convergence Rates and Guarantees. CoRR Scott Pesme, Giacomo Meanti, Michael Arbel, Julien Mairal
2025	J	`jnl`	Simulation-based inference of yeast centromeres. CoRR Eloïse Touron, Pedro L. C. Rodrigues, Julyan Arbel, Nelle Varoquaux, Michael Arbel
2025	J	`jnl`	Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching. CoRR Giacomo Meanti, Thomas Ryckeboer, Michael Arbel, Julien Mairal
2024	A*	`conf`	Functional Bilevel Optimization for Machine Learning. NeurIPS Ieva Petrulionyte, Julien Mairal, Michael Arbel
2024	J	`jnl`	Functional Bilevel Optimization for Machine Learning. CoRR Ieva Petrulionyte, Julien Mairal, Michael Arbel
2024	J	`jnl`	LUDVIG: Learning-free Uplifting of 2D Visual features to Gaussian Splatting scenes. CoRR Juliette Marrie, Romain Menegaux, Michael Arbel, Diane Larlus, Julien Mairal
2024	J	`jnl`	MLXP: A framework for conducting replicable Machine Learning eXperiments in Python. CoRR Michael Arbel, Alexandre Zouaoui
2024	—	`conf`	MLXP: A framework for conducting replicable experiments in Python. ACM-REP Michael Arbel, Alexandre Zouaoui
2024	J	`jnl`	On Good Practices for Task-Specific Distillation of Large Pretrained Models. CoRR Juliette Marrie, Michael Arbel, Julien Mairal, Diane Larlus
2024	J	`jnl`	On Good Practices for Task-Specific Distillation of Large Pretrained Visual Models. Trans. Mach. Learn. Res. Juliette Marrie, Michael Arbel, Julien Mairal, Diane Larlus
2023	A*	`conf`	Rethinking Gauss-Newton for learning over-parameterized models. NeurIPS Michael Arbel, Romain Menegaux, Pierre Wolinski
2023	J	`jnl`	Rethinking Gauss-Newton for learning over-parameterized models. CoRR Michael Arbel
2023	A*	`conf`	SLACK: Stable Learning of Augmentations with Cold-Start and KL Regularization. CVPR Juliette Marrie, Michael Arbel, Diane Larlus, Julien Mairal
2023	J	`jnl`	SLACK: Stable Learning of Augmentations with Cold-start and KL regularization. CoRR Juliette Marrie, Michael Arbel, Diane Larlus, Julien Mairal
2022	A*	`conf`	Amortized Implicit Differentiation for Stochastic Bilevel Optimization. ICLR Michael Arbel, Julien Mairal
2022	A*	`conf`	Continual Repeated Annealed Flow Transport Monte Carlo. ICML Alexander G. de G. Matthews, Michael Arbel, Danilo Jimenez Rezende, Arnaud Doucet
2022	J	`jnl`	Continual Repeated Annealed Flow Transport Monte Carlo. CoRR Alexander G. de G. Matthews, Michael Arbel, Danilo J. Rezende, Arnaud Doucet
2022	J	`jnl`	Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference. CoRR Pierre Glaser, Michael Arbel, Arnaud Doucet, Arthur Gretton
2022	A*	`conf`	Non-Convex Bilevel Games with Critical Point Selection Maps. NeurIPS Michael Arbel, Julien Mairal
2022	A	`conf`	Towards an Understanding of Default Policies in Multitask Policy Optimization. AISTATS Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano
2021	J	`jnl`	Amortized Implicit Differentiation for Stochastic Bilevel Optimization. CoRR Michael Arbel, Julien Mairal
2021	A*	`conf`	Annealed Flow Transport Monte Carlo. ICML Michael Arbel, Alexander G. de G. Matthews, Arnaud Doucet
2021	J	`jnl`	Annealed Flow Transport Monte Carlo. CoRR Michael Arbel, Alexander G. de G. Matthews, Arnaud Doucet
2021	J	`jnl`	Deep Reinforcement Learning with Dynamic Optimism. CoRR Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel
2021	A*	`conf`	Efficient Wasserstein Natural Gradients for Reinforcement Learning. ICLR Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton
2021	A*	`conf`	Generalized Energy Based Models. ICLR Michael Arbel, Liang Zhou, Arthur Gretton
2021	A*	`conf`	KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support. NeurIPS Pierre Glaser, Michael Arbel, Arthur Gretton
2021	J	`jnl`	KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support. CoRR Pierre Glaser, Michael Arbel, Arthur Gretton
2021	A*	`conf`	Tactical Optimism and Pessimism for Deep Reinforcement Learning. NeurIPS Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan
2021	A*	`conf`	The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods. ICLR Louis Thiry, Michael Arbel, Eugene Belilovsky, Edouard Oyallon
2021	J	`jnl`	The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods. CoRR Louis Thiry, Michael Arbel, Eugene Belilovsky, Edouard Oyallon
2021	J	`jnl`	Towards an Understanding of Default Policies in Multitask Policy Optimization. CoRR Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano
2020	A*	`conf`	A Non-Asymptotic Analysis for Stein Variational Gradient Descent. NeurIPS Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton
2020	J	`jnl`	A Non-Asymptotic Analysis for Stein Variational Gradient Descent. CoRR Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton
2020	J	`jnl`	Efficient Wasserstein Natural Gradients for Reinforcement Learning. CoRR Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton
2020	J	`jnl`	Estimating Barycenters of Measures in High Dimensions. CoRR Samuel Cohen, Michael Arbel, Marc Peter Deisenroth
2020	J	`jnl`	KALE: When Energy-Based Learning Meets Adversarial Training. CoRR Michael Arbel, Liang Zhou, Arthur Gretton
2020	A*	`conf`	Kernelized Wasserstein Natural Gradient. ICLR Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montúfar
2020	A*	`conf`	Synchronizing Probability Measures on Rotations via Optimal Transport. CVPR Tolga Birdal, Michael Arbel, Umut Simsekli, Leonidas J. Guibas
2020	J	`jnl`	Synchronizing Probability Measures on Rotations via Optimal Transport. CoRR Tolga Birdal, Michael Arbel, Umut Simsekli, Leonidas J. Guibas
2019	J	`jnl`	Kernelized Wasserstein Natural Gradient. CoRR Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montúfar
2019	A*	`conf`	Maximum Mean Discrepancy Gradient Flow. NeurIPS Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton
2019	J	`jnl`	Maximum Mean Discrepancy Gradient Flow. CoRR Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton
2018	—	`conf`	Demystifying MMD GANs. ICLR (Poster) Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton
2018	J	`jnl`	Demystifying MMD GANs. CoRR Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton
2018	A	`conf`	Efficient and principled score estimation with Nyström kernel exponential families. AISTATS Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton
2018	A	`conf`	Kernel Conditional Exponential Family. AISTATS Michael Arbel, Arthur Gretton
2018	A*	`conf`	On gradient regularizers for MMD GANs. NeurIPS Michael Arbel, Danica J. Sutherland, Mikolaj Binkowski, Arthur Gretton
2018	J	`jnl`	On gradient regularizers for MMD GANs. CoRR Michael Arbel, Danica J. Sutherland, Mikolaj Binkowski, Arthur Gretton
2017	J	`jnl`	Efficient and principled score estimation. CoRR Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton