Eric Vanden-Eijnden

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 45

Top Topics

Markov Chain Monte Carlo Methods

Evolution Equation

Importance Sampling

Top Venues

Multiscale Model. Simul.

Publications

Huan Zhang, Yifan Chen, Eric Vanden-Eijnden, Benjamin Peherstorfer
Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations. CoRR (2024)
Hugo Cui, Florent Krzakala, Eric Vanden-Eijnden, Lenka Zdeborová
Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity. ICLR (2024)
Yifan Chen, Mark Goldstein, Mengjian Hua, Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes. CoRR (2024)
Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden
Neural Galerkin schemes with active learning for high-dimensional evolution equations. J. Comput. Phys. 496 (2024)
Michael S. Albergo, Nicholas Matthew Boffi, Michael Lindsey, Eric Vanden-Eijnden
Multimarginal Generative Modeling with Stochastic Interpolants. ICLR (2024)
Nicholas M. Boffi, Michael S. Albergo, Eric Vanden-Eijnden
Flow Map Matching. CoRR (2024)
Nanye Ma, Mark Goldstein, Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden, Saining Xie
SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers. CoRR (2024)
Hugo Cui, Florent Krzakala, Eric Vanden-Eijnden, Lenka Zdeborová
Analysis of learning a flow-based generative model from limited sample complexity. CoRR (2023)
Nicholas M. Boffi, Eric Vanden-Eijnden
Probability flow solution of the Fokker-Planck equation. Mach. Learn. Sci. Technol. 4 (3) (2023)
Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden
Stochastic Interpolants: A Unifying Framework for Flows and Diffusions. CoRR (2023)
Nicholas M. Boffi, Eric Vanden-Eijnden
Deep learning probability flows and entropy production rates in active matter. CoRR (2023)
Davide Carbone, Mengjian Hua, Simon Coste, Eric Vanden-Eijnden
Efficient Training of Energy-Based Models Using Jarzynski Equality. CoRR (2023)
Michael S. Albergo, Eric Vanden-Eijnden
Learning to Sample Better. CoRR (2023)
Michael S. Albergo, Mark Goldstein, Nicholas M. Boffi, Rajesh Ranganath, Eric Vanden-Eijnden
Stochastic interpolants with data-dependent couplings. CoRR (2023)
Michael S. Albergo, Eric Vanden-Eijnden
Building Normalizing Flows with Stochastic Interpolants. ICLR (2023)
Michael S. Albergo, Nicholas M. Boffi, Michael Lindsey, Eric Vanden-Eijnden
Multimarginal generative modeling with stochastic interpolants. CoRR (2023)
Davide Carbone, Mengjian Hua, Simon Coste, Eric Vanden-Eijnden
Efficient Training of Energy-Based Models Using Jarzynski Equality. NeurIPS (2023)
Yuxiao Wen, Eric Vanden-Eijnden, Benjamin Peherstorfer
Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes. CoRR (2023)
Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden
Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations. CoRR (2022)
Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna
On Feature Learning in Neural Networks with Global Convergence Guarantees. CoRR (2022)
Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna
A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks. CoRR (2022)
Nicholas M. Boffi, Eric Vanden-Eijnden
Probability flow solution of the Fokker-Planck equation. CoRR (2022)
Leonardo Petrini, Francesco Cagnetta, Eric Vanden-Eijnden, Matthieu Wyart
Learning sparse features can lead to overfitting in neural networks. NeurIPS (2022)
Michael S. Albergo, Eric Vanden-Eijnden
Building Normalizing Flows with Stochastic Interpolants. CoRR (2022)
Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna
On feature learning in neural networks with global convergence guarantees. ICLR (2022)
Leonardo Petrini, Francesco Cagnetta, Eric Vanden-Eijnden, Matthieu Wyart
Learning sparse features can lead to overfitting in neural networks. CoRR (2022)
Yu Cao, Eric Vanden-Eijnden
Learning Optimal Flows for Non-Equilibrium Importance Sampling. NeurIPS (2022)
Carles Domingo-Enrich, Alberto Bietti, Marylou Gabrié, Joan Bruna, Eric Vanden-Eijnden
Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks. CoRR (2021)
Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden, Joan Bruna
On Energy-Based Models with Overparametrized Shallow Neural Networks. ICML (2021)
Shanyin Tong, Eric Vanden-Eijnden, Georg Stadler
Estimating earthquake-induced tsunami inundation probabilities without sampling. CoRR (2021)
Marylou Gabrié, Grant M. Rotskoff, Eric Vanden-Eijnden
Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods. CoRR (2021)
Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden, Joan Bruna
On Energy-Based Models with Overparametrized Shallow Neural Networks. CoRR (2021)
Grant M. Rotskoff, Andrew R. Mitchell, Eric Vanden-Eijnden
Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and Generalization. MSML (2021)
Zhengdao Chen, Grant M. Rotskoff, Joan Bruna, Eric Vanden-Eijnden
A Dynamical Central Limit Theorem for Shallow Neural Networks. NeurIPS (2020)
Zhengdao Chen, Grant M. Rotskoff, Joan Bruna, Eric Vanden-Eijnden
A Dynamical Central Limit Theorem for Shallow Neural Networks. CoRR (2020)
Stefano Sarao Mannelli, Eric Vanden-Eijnden, Lenka Zdeborová
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions. NeurIPS (2020)
Shanyin Tong, Eric Vanden-Eijnden, Georg Stadler
Extreme event probability estimation using PDE-constrained optimization and large deviation theory, with application to tsunamis. CoRR (2020)
Stefano Sarao Mannelli, Eric Vanden-Eijnden, Lenka Zdeborová
Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions. CoRR (2020)
Grant M. Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden
Global convergence of neuron birth-death dynamics. CoRR (2019)
Grant M. Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden
Neuron birth-death dynamics accelerates gradient descent and converges asymptotically. ICML (2019)
Giovanni Dematteis, Tobias Grafke, Eric Vanden-Eijnden
Extreme Event Quantification in Dynamical Systems with Random Components. SIAM/ASA J. Uncertain. Quantification 7 (3) (2019)
Grant M. Rotskoff, Eric Vanden-Eijnden
Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks. NeurIPS (2018)
Grant M. Rotskoff, Eric Vanden-Eijnden
Neural Networks as Interacting Particle Systems: Asymptotic Convexity of the Loss Landscape and Universal Scaling of the Approximation Error. CoRR (2018)
Katherine Newhall, Eric Vanden-Eijnden
Metastability of the Nonlinear Wave Equation: Insights from Transition State Theory. J. Nonlinear Sci. 27 (3) (2017)
Xiongwu Wu, Bernard R. Brooks, Eric Vanden-Eijnden
Self-guided Langevin dynamics via generalized Langevin equation. J. Comput. Chem. 37 (6) (2016)
Nawaf Bou-Rabee, Aleksandar Donev, Eric Vanden-Eijnden
Metropolis Integration Schemes for Self-Adjoint Diffusions. Multiscale Model. Simul. 12 (2) (2014)
Tobias Grafke, Rainer Grauer, T. Schäfer, Eric Vanden-Eijnden
Arclength Parametrized Hamilton's Equations for the Calculation of Instantons. Multiscale Model. Simul. 12 (2) (2014)
Maria Cameron, Eric Vanden-Eijnden
Flows in Complex Networks: Theory, Algorithms, and Application to Lennard-Jones Cluster Rearrangement. CoRR (2014)
Nawaf Bou-Rabee, Eric Vanden-Eijnden
A patch that imparts unconditional stability to explicit integrators for Langevin-like equations. J. Comput. Phys. 231 (6) (2012)
Assyr Abdulle, Weinan E, Björn Engquist, Eric Vanden-Eijnden
The heterogeneous multiscale method. Acta Numer. 21 (2012)
Maria Cameron, Robert V. Kohn, Eric Vanden-Eijnden
The String Method as a Dynamical System. J. Nonlinear Sci. 21 (2) (2011)
Daan Crommelin, Eric Vanden-Eijnden
Diffusion Estimation from Multiscale Data by Operator Eigenpairs. Multiscale Model. Simul. 9 (4) (2011)
Juan P. Agnelli, M. Cadeiras, Esteban G. Tabak, Cristina Vilma Turner, Eric Vanden-Eijnden
Clustering and Classification through Normalizing Flows in Feature Space. Multiscale Model. Simul. 8 (5) (2010)
Marco Sarich, Christof Schütte, Eric Vanden-Eijnden
Optimal Fuzzy Aggregation of Networks. Multiscale Model. Simul. 8 (4) (2010)
Weinan E, Weiqing Ren, Eric Vanden-Eijnden
A general strategy for designing seamless multiscale methods. J. Comput. Phys. 228 (15) (2009)
Daan Crommelin, Eric Vanden-Eijnden
Data-Based Inference of Generators for Markov Jump Processes Using Convex Optimization. Multiscale Model. Simul. 7 (4) (2009)
Eric Vanden-Eijnden
Some recent techniques for free energy calculations. J. Comput. Chem. 30 (11) (2009)
Yvo Pokern, Andrew M. Stuart, Eric Vanden-Eijnden
Remarks on Drift Estimation for Diffusion Processes. Multiscale Model. Simul. 8 (1) (2009)
Gil Ariel, Eric Vanden-Eijnden
Accelerated Simulation of a Heavy Particle in a Gas of Elastic Spheres. Multiscale Model. Simul. 7 (1) (2008)
Philipp Metzner, Christof Schütte, Eric Vanden-Eijnden
Transition Path Theory for Markov Jump Processes. Multiscale Model. Simul. 7 (3) (2008)
Weinan E, Di Liu, Eric Vanden-Eijnden
Nested stochastic simulation algorithms for chemical kinetic systems with multiple time scales. J. Comput. Phys. 221 (1) (2007)
Daan Crommelin, Eric Vanden-Eijnden
Fitting timeseries by continuous-time Markov chains: A quadratic programming approach. J. Comput. Phys. 217 (2) (2006)
Robert V. Kohn, Maria G. Reznikoff, Eric Vanden-Eijnden
Magnetic Elements at Finite Temperature and Large Deviation Theory. J. Nonlinear Sci. 15 (4) (2005)