← Authors

Michael Arbel

22 papers · 2018–2025 · 6 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (14) 🐣 Hot Topic Early Bird 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (35) 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (7) 🌈 Renaissance Researcher (6) πŸ‘‘ Triple Crown πŸ”₯ Unstoppable (8) πŸ’Ž Century Club (22) πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (80) ⚑ Prolific Year (6)

Conferences

NIPS (8) ICLR (5) AISTATS (3) CVPR (2) ICCV (2) ICML (2)

Top co-authors

Arthur Gretton (9) Julien Mairal (6) Ted Moskovitz (3) Danica J. Sutherland (3) Aldo Pacchiano (2) Jack Parker-Holder (2) Juliette Marrie (2) MikoΕ‚aj BiΕ„kowski (2) Romain Menegaux (2) Adil SALIM (2)

Research topics

Optimization & Theory (1)

Keywords

kernel methods (6) bilevel optimization (3) neural network (3) maximum mean discrepancy (3) markov chain monte carlo (2) kl divergence (2) variational inference (2) annealed importance sampling (2) normalizing flow (2) exponential family (2) reproducing kernel hilbert space (2) reinforcement learning (2) sequential monte carlo (2) global convergence (1) data augmentation (1) optimal transport (1) image generation (1) image classification (1) policy gradient (1) natural gradient (1)

Papers

Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching ICCV 2025
LUDVIG: Learning-Free Uplifting of 2D Visual Features to Gaussian Splatting Scenes ICCV 2025
Functional Bilevel Optimization for Machine Learning NIPS 2024
SLACK: Stable Learning of Augmentations With Cold-Start and KL Regularization CVPR 2023
Rethinking Gauss-Newton for learning over-parameterized models NIPS 2023
Amortized Implicit Differentiation for Stochastic Bilevel Optimization ICLR 2022
Towards an Understanding of Default Policies in Multitask Policy Optimization AISTATS 2022
Non-Convex Bilevel Games with Critical Point Selection Maps NIPS 2022
Continual Repeated Annealed Flow Transport Monte Carlo ICML 2022
Efficient Wasserstein Natural Gradients for Reinforcement Learning ICLR 2021
Tactical Optimism and Pessimism for Deep Reinforcement Learning NIPS 2021
Generalized Energy Based Models ICLR 2021
Annealed Flow Transport Monte Carlo ICML 2021
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods ICLR 2021
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support NIPS 2021
Synchronizing Probability Measures on Rotations via Optimal Transport CVPR 2020
A Non-Asymptotic Analysis for Stein Variational Gradient Descent NIPS 2020
Maximum Mean Discrepancy Gradient Flow NIPS 2019
Demystifying MMD GANs ICLR 2018
On gradient regularizers for MMD GANs NIPS 2018
Kernel Conditional Exponential Family AISTATS 2018
Efficient and principled score estimation with NystrΓΆm kernel exponential families AISTATS 2018