Eric Vanden-Eijnden
18 papers · 2018–2025 · 6 conferences · across top CS/AI conferences
Achievements
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🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🏃 Academic Marathon (7) 🐝 Cross-Pollinator (12)
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Taxonomy Completionist
(19)
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Conference Polyglot
(6)
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Century Club
(18)
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Unstoppable
(8)
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Prolific Year
(5)
Conferences
NIPS (6)
ICML (5)
ICLR (4)
ECCV (1)
JMLR (1)
UAI (1)
Top co-authors
Keywords
neural network
(5)
generalization error
(3)
gradient descent
(3)
shallow neural network
(2)
energy-based model
(2)
maximum likelihood
(1)
markov chain monte carlo
(1)
mean field theory
(1)
sequential monte carlo
(1)
sparse representation
(1)
importance sampling
(1)
variance reduction
(1)
generative model
(1)
diffusion model
(1)
target distribution
(1)
partial differential equation
(1)
random feature
(1)
mean-field limit
(1)
stochastic differential equation
(1)
asymptotic analysis
(1)
Papers
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
Multimarginal Generative Modeling with Stochastic Interpolants
ICLR 2024
SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
ECCV 2024
Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity
ICLR 2024
Stochastic Interpolants with Data-Dependent Couplings
ICML 2024
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
ICML 2024
Building Normalizing Flows with Stochastic Interpolants
ICLR 2023
Efficient Training of Energy-Based Models Using Jarzynski Equality
NIPS 2023
On feature learning in neural networks with global convergence guarantees
ICLR 2022
Learning sparse features can lead to overfitting in neural networks
NIPS 2022
Learning Optimal Flows for Non-Equilibrium Importance Sampling
NIPS 2022
On Energy-Based Models with Overparametrized Shallow Neural Networks
ICML 2021
A Dynamical Central Limit Theorem for Shallow Neural Networks
NIPS 2020
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions
NIPS 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
NIPS 2018