Efficient and principled score estimation. - Danica J. Sutherland; Heiko Strathmann; Michael Arbel; Arthur Gretton (CoRR, 2017)

Kernel Conditional Exponential Family. - 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)

Demystifying MMD GANs. - Mikolaj Binkowski; Danica J. Sutherland; Michael Arbel; Arthur Gretton (ICLR (Poster), 2018)

On gradient regularizers for MMD GANs. - Michael Arbel; Danica J. Sutherland; Mikolaj Binkowski; Arthur Gretton (NeurIPS, 2018)

Maximum Mean Discrepancy Gradient Flow. - Michael Arbel; Anna Korba; Adil Salim; Arthur Gretton (NeurIPS, 2019)

Synchronizing Probability Measures on Rotations via Optimal Transport. - Tolga Birdal; Michael Arbel; Umut Simsekli; Leonidas J. Guibas (CVPR, 2020)

Kernelized Wasserstein Natural Gradient. - Michael Arbel; Arthur Gretton; Wuchen Li; Guido Montúfar (ICLR, 2020)

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

KALE: When Energy-Based Learning Meets Adversarial Training. - Michael Arbel; Liang Zhou; Arthur Gretton (CoRR, 2020)

Estimating Barycenters of Measures in High Dimensions. - Samuel Cohen; Michael Arbel; Marc Peter Deisenroth (CoRR, 2020)

Generalized Energy Based Models. - Michael Arbel; Liang Zhou; Arthur Gretton (ICLR, 2021)

Efficient Wasserstein Natural Gradients for Reinforcement Learning. - Ted Moskovitz; Michael Arbel; Ferenc Huszar; Arthur Gretton (ICLR, 2021)

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

Annealed Flow Transport Monte Carlo. - Michael Arbel; Alexander G. de G. Matthews; Arnaud Doucet (ICML, 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 (NeurIPS, 2021)

Deep Reinforcement Learning with Dynamic Optimism. - Ted Moskovitz; Jack Parker-Holder; Aldo Pacchiano; Michael Arbel (CoRR, 2021)

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

Amortized Implicit Differentiation for Stochastic Bilevel Optimization. - Michael Arbel; Julien Mairal (ICLR, 2022)

Continual Repeated Annealed Flow Transport Monte Carlo. - Alexander G. de G. Matthews; Michael Arbel; Danilo Jimenez Rezende; Arnaud Doucet (ICML, 2022)

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

Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference. - Pierre Glaser; Michael Arbel; Arnaud Doucet; Arthur Gretton (CoRR, 2022)

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

Rethinking Gauss-Newton for learning over-parameterized models. - Michael Arbel; Romain Menegaux; Pierre Wolinski (NeurIPS, 2023)

Rethinking Gauss-Newton for learning over-parameterized models. - Michael Arbel (CoRR, 2023)

MLXP: A framework for conducting replicable experiments in Python. - Michael Arbel; Alexandre Zouaoui (ACM-REP, 2024)

Functional Bilevel Optimization for Machine Learning. - Ieva Petrulionyte; Julien Mairal; Michael Arbel (NeurIPS, 2024)

On Good Practices for Task-Specific Distillation of Large Pretrained Models. - Juliette Marrie; Michael Arbel; Julien Mairal; Diane Larlus (CoRR, 2024)

MLXP: A framework for conducting replicable Machine Learning eXperiments in Python. - Michael Arbel; Alexandre Zouaoui (CoRR, 2024)

LUDVIG: Learning-free Uplifting of 2D Visual features to Gaussian Splatting scenes. - Juliette Marrie; Romain Menegaux; Michael Arbel; Diane Larlus; Julien Mairal (CoRR, 2024)

On Good Practices for Task-Specific Distillation of Large Pretrained Visual Models. - Juliette Marrie; Michael Arbel; Julien Mairal; Diane Larlus (Trans. Mach. Learn. Res., 2024)

EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks. - Michael Arbel; David Salinas; Frank Hutter (CoRR, 2025)

Learning Theory for Kernel Bilevel Optimization. - Fares El Khoury; Edouard Pauwels; Samuel Vaiter; Michael Arbel (CoRR, 2025)

Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching. - Giacomo Meanti; Thomas Ryckeboer; Michael Arbel; Julien Mairal (CoRR, 2025)

MAP Estimation with Denoisers: Convergence Rates and Guarantees. - Scott Pesme; Giacomo Meanti; Michael Arbel; Julien Mairal (CoRR, 2025)