Arbel, Michael

28 publications

NeurIPS 2025 EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Network Michael Arbel, David Salinas, Frank Hutter

ICCV 2025 LUDVIG: Learning-Free Uplifting of 2D Visual Features to Gaussian Splatting Scenes Juliette Marrie, Romain Menegaux, Michael Arbel, Diane Larlus, Julien Mairal

NeurIPS 2025 Learning Theory for Kernel Bilevel Optimization Fares El Khoury, Edouard Pauwels, Samuel Vaiter, Michael Arbel

NeurIPS 2025 MAP Estimation with Denoisers: Convergence Rates and Guarantees Scott Pesme, Giacomo Meanti, Michael Arbel, Julien Mairal

ICCV 2025 Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching Giacomo Meanti, Thomas Ryckeboer, Michael Arbel, Julien Mairal

NeurIPS 2024 Functional Bilevel Optimization for Machine Learning Ieva Petrulionyte, Julien Mairal, Michael Arbel

TMLR 2024 On Good Practices for Task-Specific Distillation of Large Pretrained Visual Models Juliette Marrie, Michael Arbel, Julien Mairal, Diane Larlus

NeurIPSW 2023 Improving Deep Ensembles Without Communication Konstantinos Pitas, Michael Arbel, Julyan Arbel

NeurIPS 2023 Rethinking Gauss-Newton for Learning Over-Parameterized Models Michael Arbel, Romain Menegaux, Pierre Wolinski

CVPR 2023 SLACK: Stable Learning of Augmentations with Cold-Start and KL Regularization Juliette Marrie, Michael Arbel, Diane Larlus, Julien Mairal

AISTATS 2022 Towards an Understanding of Default Policies in Multitask Policy Optimization Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano

ICLR 2022 Amortized Implicit Differentiation for Stochastic Bilevel Optimization Michael Arbel, Julien Mairal

ICML 2022 Continual Repeated Annealed Flow Transport Monte Carlo Alex Matthews, Michael Arbel, Danilo Jimenez Rezende, Arnaud Doucet

NeurIPSW 2022 Fair Synthetic Data Does Not Necessarily Lead to Fair Models Yam Eitan, Nathan Cavaglione, Michael Arbel, Samuel Cohen

NeurIPS 2022 Non-Convex Bilevel Games with Critical Point Selection Maps Michael Arbel, Julien Mairal

ICML 2021 Annealed Flow Transport Monte Carlo Michael Arbel, Alex Matthews, Arnaud Doucet

ICLR 2021 Efficient Wasserstein Natural Gradients for Reinforcement Learning Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton

ICLR 2021 Generalized Energy Based Models Michael Arbel, Liang Zhou, Arthur Gretton

NeurIPS 2021 KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support Pierre Glaser, Michael Arbel, Arthur Gretton

NeurIPS 2021 Tactical Optimism and Pessimism for Deep Reinforcement Learning Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan

ICLR 2021 The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods Louis Thiry, Michael Arbel, Eugene Belilovsky, Edouard Oyallon

NeurIPS 2020 A Non-Asymptotic Analysis for Stein Variational Gradient Descent Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton

ICLR 2020 Kernelized Wasserstein Natural Gradient Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montufar

NeurIPS 2019 Maximum Mean Discrepancy Gradient Flow Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton

ICLR 2018 Demystifying MMD GANs Mikołaj Bińkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton

AISTATS 2018 Efficient and Principled Score Estimation with Nyström Kernel Exponential Families Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton

AISTATS 2018 Kernel Conditional Exponential Family Michael Arbel, Arthur Gretton

NeurIPS 2018 On Gradient Regularizers for MMD GANs Michael Arbel, Danica J. Sutherland, Mikołaj Bińkowski, Arthur Gretton