Michael Arbel

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

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Achievements

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🗺️ Taxonomy Completionist (40) 🏃 Academic Marathon · Sprint 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird

👑 Triple Crown 🚀 Conference Pioneer 🔥 Unstoppable · Bronze ⚡ Prolific Year · Productive 💎 Century Club · Bronze 📈 Trend Setter 🗃️ Keyword Collector (96)

Venues

NeurIPS (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) Mikołaj Bińkowski (2) Romain Menegaux (2) Adil SALIM (2)

Research topics

Machine Learning (19) Optimization & Theory · Machine Learning (12) Deep Learning (9) Artificial Intelligence (6) Mathematics & Optimization (6) Core Methods (6) Learning Types · Machine Learning (6) Optimization (5) Computer Vision (4) Models (4) Optimization & Theory · Deep Learning (4) Bayesian & Probabilistic (3) Core AI (3) Learning Types · Deep Learning (3) Reinforcement Learning (2)

Keywords

kernel methods (7) bilevel optimization (4) maximum mean discrepancy (4) neural network (3) reinforcement learning (3) markov chain monte carlo (3) normalizing flow (3) density estimation (2) annealed importance sampling (2) image classification (2) variational inference (2) implicit differentiation (2) exponential family (2) generative adversarial network (2) policy gradient (2) reproducing kernel hilbert space (2) image generation (2) sequential monte carlo (2) kl divergence (2) object recognition (1)

Papers

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