Eric Vanden-Eijnden
19 papers · 2018–2026 · 6 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (26) 🏃 Academic Marathon · Sprint 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
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Keyword Champion
(3)
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Topic Evolution
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Deep Specialist
(10)
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Prolific Year · Productive
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Century Club · Bronze
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Unstoppable · Bronze
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Research topics
Deep Learning
(16)
Machine Learning
(15)
Optimization & Theory · Machine Learning
(14)
Models
(11)
Mathematics & Optimization
(4)
Optimization
(4)
Optimization & Theory · Deep Learning
(4)
Artificial Intelligence
(2)
Architectures
(2)
Bayesian & Probabilistic · Artificial Intelligence
(2)
Core Methods
(2)
Learning Types
(2)
Computer Vision
(1)
Bayesian & Probabilistic · Machine Learning
(1)
Processing
(1)
Keywords
neural network
(6)
gradient descent
(3)
generalization error
(3)
stochastic interpolant
(3)
feature learning
(3)
velocity field
(3)
generative model
(3)
generative modeling
(2)
square loss regression
(2)
mean field theory
(2)
shallow neural network
(2)
diffusion model
(2)
optimal transport
(2)
energy-based model
(2)
diffusion process
(1)
importance sampling
(1)
sparse representation
(1)
asymptotic analysis
(1)
maximum likelihood
(1)
neural tangent kernel
(1)
Papers
A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks
JMLR 2026
NETS: A Non-equilibrium Transport Sampler
ICML 2025
Simulation-Free Differential Dynamics Through Neural Conservation Laws
UAI 2025
Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
JMLR 2025
Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity
ICLR 2024
Multimarginal Generative Modeling with Stochastic Interpolants
ICLR 2024
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
ICML 2024
SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
ECCV 2024
Stochastic Interpolants with Data-Dependent Couplings
ICML 2024
Building Normalizing Flows with Stochastic Interpolants
ICLR 2023
Efficient Training of Energy-Based Models Using Jarzynski Equality
NeurIPS 2023
Learning Optimal Flows for Non-Equilibrium Importance Sampling
NeurIPS 2022
Learning sparse features can lead to overfitting in neural networks
NeurIPS 2022
On feature learning in neural networks with global convergence guarantees
ICLR 2022
On Energy-Based Models with Overparametrized Shallow Neural Networks
ICML 2021
A Dynamical Central Limit Theorem for Shallow Neural Networks
NeurIPS 2020
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions
NeurIPS 2020
Neuron birth-death dynamics accelerates gradient descent and converges asymptotically
ICML 2019
Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks
NeurIPS 2018