Magnetic Elements at Finite Temperature and Large Deviation Theory. - Robert V. Kohn; Maria G. Reznikoff; Eric Vanden-Eijnden (J. Nonlinear Sci., 2005)

Fitting timeseries by continuous-time Markov chains: A quadratic programming approach. - Daan Crommelin; Eric Vanden-Eijnden (J. Comput. Phys., 2006)

Nested stochastic simulation algorithms for chemical kinetic systems with multiple time scales. - Weinan E; Di Liu; Eric Vanden-Eijnden (J. Comput. Phys., 2007)

Accelerated Simulation of a Heavy Particle in a Gas of Elastic Spheres. - Gil Ariel; Eric Vanden-Eijnden (Multiscale Model. Simul., 2008)

Transition Path Theory for Markov Jump Processes. - Philipp Metzner; Christof Schütte; Eric Vanden-Eijnden (Multiscale Model. Simul., 2008)

Some recent techniques for free energy calculations. - Eric Vanden-Eijnden (J. Comput. Chem., 2009)

A general strategy for designing seamless multiscale methods. - Weinan E; Weiqing Ren; Eric Vanden-Eijnden (J. Comput. Phys., 2009)

Data-Based Inference of Generators for Markov Jump Processes Using Convex Optimization. - Daan Crommelin; Eric Vanden-Eijnden (Multiscale Model. Simul., 2009)

Remarks on Drift Estimation for Diffusion Processes. - Yvo Pokern; Andrew M. Stuart; Eric Vanden-Eijnden (Multiscale Model. Simul., 2009)

Clustering and Classification through Normalizing Flows in Feature Space. - Juan P. Agnelli; M. Cadeiras; Esteban G. Tabak; Cristina Vilma Turner; Eric Vanden-Eijnden (Multiscale Model. Simul., 2010)

Optimal Fuzzy Aggregation of Networks. - Marco Sarich; Christof Schütte; Eric Vanden-Eijnden (Multiscale Model. Simul., 2010)

The String Method as a Dynamical System. - Maria Cameron; Robert V. Kohn; Eric Vanden-Eijnden (J. Nonlinear Sci., 2011)

Diffusion Estimation from Multiscale Data by Operator Eigenpairs. - Daan Crommelin; Eric Vanden-Eijnden (Multiscale Model. Simul., 2011)

The heterogeneous multiscale method. - Assyr Abdulle; Weinan E; Björn Engquist; Eric Vanden-Eijnden (Acta Numer., 2012)

A patch that imparts unconditional stability to explicit integrators for Langevin-like equations. - Nawaf Bou-Rabee; Eric Vanden-Eijnden (J. Comput. Phys., 2012)

Flows in Complex Networks: Theory, Algorithms, and Application to Lennard-Jones Cluster Rearrangement. - Maria Cameron; Eric Vanden-Eijnden (CoRR, 2014)

Metropolis Integration Schemes for Self-Adjoint Diffusions. - Nawaf Bou-Rabee; Aleksandar Donev; Eric Vanden-Eijnden (Multiscale Model. Simul., 2014)

Arclength Parametrized Hamilton's Equations for the Calculation of Instantons. - Tobias Grafke; Rainer Grauer; T. Schäfer; Eric Vanden-Eijnden (Multiscale Model. Simul., 2014)

Self-guided Langevin dynamics via generalized Langevin equation. - Xiongwu Wu; Bernard R. Brooks; Eric Vanden-Eijnden (J. Comput. Chem., 2016)

Metastability of the Nonlinear Wave Equation: Insights from Transition State Theory. - Katherine Newhall; Eric Vanden-Eijnden (J. Nonlinear Sci., 2017)

Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks. - Grant M. Rotskoff; Eric Vanden-Eijnden (NeurIPS, 2018)

Neural Networks as Interacting Particle Systems: Asymptotic Convexity of the Loss Landscape and Universal Scaling of the Approximation Error. - Grant M. Rotskoff; Eric Vanden-Eijnden (CoRR, 2018)

Neuron birth-death dynamics accelerates gradient descent and converges asymptotically. - Grant M. Rotskoff; Samy Jelassi; Joan Bruna; Eric Vanden-Eijnden (ICML, 2019)

Global convergence of neuron birth-death dynamics. - Grant M. Rotskoff; Samy Jelassi; Joan Bruna; Eric Vanden-Eijnden (CoRR, 2019)

Extreme Event Quantification in Dynamical Systems with Random Components. - Giovanni Dematteis; Tobias Grafke; Eric Vanden-Eijnden (SIAM/ASA J. Uncertain. Quantification, 2019)

A Dynamical Central Limit Theorem for Shallow Neural Networks. - Zhengdao Chen; Grant M. Rotskoff; Joan Bruna; Eric Vanden-Eijnden (NeurIPS, 2020)

Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions. - Stefano Sarao Mannelli; Eric Vanden-Eijnden; Lenka Zdeborová (NeurIPS, 2020)

Extreme event probability estimation using PDE-constrained optimization and large deviation theory, with application to tsunamis. - Shanyin Tong; Eric Vanden-Eijnden; Georg Stadler (CoRR, 2020)

On Energy-Based Models with Overparametrized Shallow Neural Networks. - Carles Domingo-Enrich; Alberto Bietti; Eric Vanden-Eijnden; Joan Bruna (ICML, 2021)

Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and Generalization. - Grant M. Rotskoff; Andrew R. Mitchell; Eric Vanden-Eijnden (MSML, 2021)

Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks. - Carles Domingo-Enrich; Alberto Bietti; Marylou Gabrié; Joan Bruna; Eric Vanden-Eijnden (CoRR, 2021)

Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods. - Marylou Gabrié; Grant M. Rotskoff; Eric Vanden-Eijnden (CoRR, 2021)

Estimating earthquake-induced tsunami inundation probabilities without sampling. - Shanyin Tong; Eric Vanden-Eijnden; Georg Stadler (CoRR, 2021)

On feature learning in neural networks with global convergence guarantees. - Zhengdao Chen; Eric Vanden-Eijnden; Joan Bruna (ICLR, 2022)

Learning Optimal Flows for Non-Equilibrium Importance Sampling. - Yu Cao; Eric Vanden-Eijnden (NeurIPS, 2022)

Learning sparse features can lead to overfitting in neural networks. - Leonardo Petrini; Francesco Cagnetta; Eric Vanden-Eijnden; Matthieu Wyart (NeurIPS, 2022)

Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations. - Joan Bruna; Benjamin Peherstorfer; Eric Vanden-Eijnden (CoRR, 2022)

A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks. - Zhengdao Chen; Eric Vanden-Eijnden; Joan Bruna (CoRR, 2022)

Building Normalizing Flows with Stochastic Interpolants. - Michael S. Albergo; Eric Vanden-Eijnden (ICLR, 2023)

Efficient Training of Energy-Based Models Using Jarzynski Equality. - Davide Carbone; Mengjian Hua; Simon Coste; Eric Vanden-Eijnden (NeurIPS, 2023)

Stochastic Interpolants: A Unifying Framework for Flows and Diffusions. - Michael S. Albergo; Nicholas M. Boffi; Eric Vanden-Eijnden (CoRR, 2023)

Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes. - Yuxiao Wen; Eric Vanden-Eijnden; Benjamin Peherstorfer (CoRR, 2023)

Deep learning probability flows and entropy production rates in active matter. - Nicholas M. Boffi; Eric Vanden-Eijnden (CoRR, 2023)

Learning to Sample Better. - Michael S. Albergo; Eric Vanden-Eijnden (CoRR, 2023)

Probability flow solution of the Fokker-Planck equation. - Nicholas M. Boffi; Eric Vanden-Eijnden (Mach. Learn. Sci. Technol., 2023)

SiT: Exploring Flow and Diffusion-Based Generative Models with Scalable Interpolant Transformers. - Nanye Ma; Mark Goldstein; Michael S. Albergo; Nicholas M. Boffi; Eric Vanden-Eijnden; Saining Xie (ECCV (77), 2024)

Multimarginal Generative Modeling with Stochastic Interpolants. - Michael S. Albergo; Nicholas Matthew Boffi; Michael Lindsey; Eric Vanden-Eijnden (ICLR, 2024)

Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity. - Hugo Cui; Florent Krzakala; Eric Vanden-Eijnden; Lenka Zdeborová (ICLR, 2024)

Stochastic Interpolants with Data-Dependent Couplings. - Michael S. Albergo; Mark Goldstein; Nicholas Matthew Boffi; Rajesh Ranganath; Eric Vanden-Eijnden (ICML, 2024)

Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes. - Yifan Chen; Mark Goldstein; Mengjian Hua; Michael S. Albergo; Nicholas Matthew Boffi; Eric Vanden-Eijnden (ICML, 2024)

Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations. - Huan Zhang; Yifan Chen; Eric Vanden-Eijnden; Benjamin Peherstorfer (CoRR, 2024)

Flow Map Matching. - Nicholas M. Boffi; Michael S. Albergo; Eric Vanden-Eijnden (CoRR, 2024)

NETS: A Non-Equilibrium Transport Sampler. - Michael S. Albergo; Eric Vanden-Eijnden (CoRR, 2024)

A Simulation-Free Deep Learning Approach to Stochastic Optimal Control. - Mengjian Hua; Mathieu Laurière; Eric Vanden-Eijnden (CoRR, 2024)

Model-free learning of probability flows: Elucidating the nonequilibrium dynamics of flocking. - Nicholas M. Boffi; Eric Vanden-Eijnden (CoRR, 2024)

Neural Galerkin schemes with active learning for high-dimensional evolution equations. - Joan Bruna; Benjamin Peherstorfer; Eric Vanden-Eijnden (J. Comput. Phys., 2024)

Simulation-Free Differential Dynamics Through Neural Conservation Laws. - Mengjian Hua; Eric Vanden-Eijnden; Ricky T. Q. Chen (UAI, 2025)

Flow map matching with stochastic interpolants: A mathematical framework for consistency models. - Nicholas Matthew Boffi; Michael Samuel Albergo; Eric Vanden-Eijnden (Trans. Mach. Learn. Res., 2025)

Optimizing Noise Schedules of Generative Models in High Dimensionss. - Santiago Aranguri; Giulio Biroli; Marc Mézard; Eric Vanden-Eijnden (CoRR, 2025)

FEAT: Free energy Estimators with Adaptive Transport. - Jiajun He 003; Yuanqi Du; Francisco Vargas 001; Yuanqing Wang; Carla P. Gomes; José Miguel Hernández-Lobato; Eric Vanden-Eijnden (CoRR, 2025)

How to build a consistency model: Learning flow maps via self-distillation. - Nicholas M. Boffi; Michael S. Albergo; Eric Vanden-Eijnden (CoRR, 2025)

Multitask Learning with Stochastic Interpolants. - Hugo Negrel; Florentin Coeurdoux; Michael S. Albergo; Eric Vanden-Eijnden (CoRR, 2025)