Michael Arbel
22 papers · 2018–2025 · 6 conferences · across top CS/AI conferences
Achievements
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π Cross-Pollinator (14) π Academic Marathon (7) π Interdisciplinary Bridge π Conference Polyglot (6) π Renaissance Researcher (6)
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Conference Polyglot
(6)
π
Academic Marathon
(7)
π
Renaissance Researcher
(6)
π
Triple Crown
π₯
Unstoppable
(8)
π
Century Club
(22)
π
Conference Pioneer
ποΈ
Keyword Collector
(80)
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Prolific Year
(6)
Conferences
NIPS (8)
ICLR (5)
AISTATS (3)
CVPR (2)
ICCV (2)
ICML (2)
Top co-authors
Research topics
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