Eric Vanden-Eijnden

87 papers A* 15A 1Journal 69Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	MGD: Moment Guided Diffusion for Maximum Entropy Generation. CoRR Etienne Lempereur, Nathanaël Cuvelle-Magar, Florentin Coeurdoux, Stéphane Mallat, Eric Vanden-Eijnden
2026	J	`jnl`	Probing the Geometry of Diffusion Models with the String Method. CoRR Elio Moreau, Florentin Coeurdoux, Grégoire Ferré, Eric Vanden-Eijnden
2026	J	`jnl`	Training-Free Generative Modeling via Kernelized Stochastic Interpolants. CoRR Florentin Coeurdoux, Etienne Lempereur, Nathanaël Cuvelle-Magar, Thomas Eboli, Stéphane Mallat, Anastasia Borovykh, Eric Vanden-Eijnden
2026	J	`jnl`	Variational Optimality of Föllmer Processes in Generative Diffusions. CoRR Yifan Chen, Eric Vanden-Eijnden
2025	J	`jnl`	Dynamic Test-Time Compute Scaling in Control Policy: Difficulty-Aware Stochastic Interpolant Policy. CoRR Inkook Chun, Seungjae Lee, Michael S. Albergo, Saining Xie, Eric Vanden-Eijnden
2025	J	`jnl`	FEAT: Free energy Estimators with Adaptive Transport. CoRR Jiajun He, Yuanqi Du, Francisco Vargas, Yuanqing Wang, Carla P. Gomes, José Miguel Hernández-Lobato, Eric Vanden-Eijnden
2025	J	`jnl`	Flow map matching with stochastic interpolants: A mathematical framework for consistency models. Trans. Mach. Learn. Res. Nicholas Matthew Boffi, Michael Samuel Albergo, Eric Vanden-Eijnden
2025	J	`jnl`	Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants. CoRR Chirag Modi, Jiequn Han, Eric Vanden-Eijnden, Joan Bruna
2025	J	`jnl`	How to build a consistency model: Learning flow maps via self-distillation. CoRR Nicholas M. Boffi, Michael S. Albergo, Eric Vanden-Eijnden
2025	J	`jnl`	Lipschitz-Guided Design of Interpolation Schedules in Generative Models. CoRR Yifan Chen, Eric Vanden-Eijnden, Jiawei Xu
2025	J	`jnl`	Multitask Learning with Stochastic Interpolants. CoRR Hugo Negrel, Florentin Coeurdoux, Michael S. Albergo, Eric Vanden-Eijnden
2025	A*	`conf`	NETS: A Non-equilibrium Transport Sampler. ICML Michael Samuel Albergo, Eric Vanden-Eijnden
2025	J	`jnl`	Optimizing Noise Schedules of Generative Models in High Dimensionss. CoRR Santiago Aranguri, Giulio Biroli, Marc Mézard, Eric Vanden-Eijnden
2025	J	`jnl`	Scale-Adaptive Generative Flows for Multiscale Scientific Data. CoRR Yifan Chen, Eric Vanden-Eijnden
2025	A	`conf`	Simulation-Free Differential Dynamics Through Neural Conservation Laws. UAI Mengjian Hua, Eric Vanden-Eijnden, Ricky T. Q. Chen
2025	J	`jnl`	Simulation-Free Differential Dynamics through Neural Conservation Laws. CoRR Mengjian Hua, Eric Vanden-Eijnden, Ricky T. Q. Chen
2025	J	`jnl`	Stochastic Interpolants: A Unifying Framework for Flows and Diffusions. J. Mach. Learn. Res. Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden
2025	J	`jnl`	Test-time scaling of diffusions with flow maps. CoRR Amirmojtaba Sabour, Michael S. Albergo, Carles Domingo-Enrich, Nicholas M. Boffi, Sanja Fidler, Karsten Kreis, Eric Vanden-Eijnden
2024	J	`jnl`	A Simulation-Free Deep Learning Approach to Stochastic Optimal Control. CoRR Mengjian Hua, Mathieu Laurière, Eric Vanden-Eijnden
2024	A*	`conf`	Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity. ICLR Hugo Cui, Florent Krzakala, Eric Vanden-Eijnden, Lenka Zdeborová
2024	J	`jnl`	Flow Map Matching. CoRR Nicholas M. Boffi, Michael S. Albergo, Eric Vanden-Eijnden
2024	J	`jnl`	Model-free learning of probability flows: Elucidating the nonequilibrium dynamics of flocking. CoRR Nicholas M. Boffi, Eric Vanden-Eijnden
2024	A*	`conf`	Multimarginal Generative Modeling with Stochastic Interpolants. ICLR Michael S. Albergo, Nicholas Matthew Boffi, Michael Lindsey, Eric Vanden-Eijnden
2024	J	`jnl`	NETS: A Non-Equilibrium Transport Sampler. CoRR Michael S. Albergo, Eric Vanden-Eijnden
2024	J	`jnl`	Neural Galerkin schemes with active learning for high-dimensional evolution equations. J. Comput. Phys. Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden
2024	A*	`conf`	Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes. ICML Yifan Chen, Mark Goldstein, Mengjian Hua, Michael S. Albergo, Nicholas Matthew Boffi, Eric Vanden-Eijnden
2024	J	`jnl`	Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes. CoRR Yifan Chen, Mark Goldstein, Mengjian Hua, Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden
2024	J	`jnl`	Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations. CoRR Huan Zhang, Yifan Chen, Eric Vanden-Eijnden, Benjamin Peherstorfer
2024	—	`conf`	SiT: Exploring Flow and Diffusion-Based Generative Models with Scalable Interpolant Transformers. ECCV (77) Nanye Ma, Mark Goldstein, Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden, Saining Xie
2024	J	`jnl`	SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers. CoRR Nanye Ma, Mark Goldstein, Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden, Saining Xie
2024	A*	`conf`	Stochastic Interpolants with Data-Dependent Couplings. ICML Michael S. Albergo, Mark Goldstein, Nicholas Matthew Boffi, Rajesh Ranganath, Eric Vanden-Eijnden
2023	J	`jnl`	Analysis of learning a flow-based generative model from limited sample complexity. CoRR Hugo Cui, Florent Krzakala, Eric Vanden-Eijnden, Lenka Zdeborová
2023	A*	`conf`	Building Normalizing Flows with Stochastic Interpolants. ICLR Michael S. Albergo, Eric Vanden-Eijnden
2023	J	`jnl`	Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes. CoRR Yuxiao Wen, Eric Vanden-Eijnden, Benjamin Peherstorfer
2023	J	`jnl`	Deep learning probability flows and entropy production rates in active matter. CoRR Nicholas M. Boffi, Eric Vanden-Eijnden
2023	A*	`conf`	Efficient Training of Energy-Based Models Using Jarzynski Equality. NeurIPS Davide Carbone, Mengjian Hua, Simon Coste, Eric Vanden-Eijnden
2023	J	`jnl`	Efficient Training of Energy-Based Models Using Jarzynski Equality. CoRR Davide Carbone, Mengjian Hua, Simon Coste, Eric Vanden-Eijnden
2023	J	`jnl`	Learning to Sample Better. CoRR Michael S. Albergo, Eric Vanden-Eijnden
2023	J	`jnl`	Multimarginal generative modeling with stochastic interpolants. CoRR Michael S. Albergo, Nicholas M. Boffi, Michael Lindsey, Eric Vanden-Eijnden
2023	J	`jnl`	Probability flow solution of the Fokker-Planck equation. Mach. Learn. Sci. Technol. Nicholas M. Boffi, Eric Vanden-Eijnden
2023	J	`jnl`	Stochastic Interpolants: A Unifying Framework for Flows and Diffusions. CoRR Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden
2023	J	`jnl`	Stochastic interpolants with data-dependent couplings. CoRR Michael S. Albergo, Mark Goldstein, Nicholas M. Boffi, Rajesh Ranganath, Eric Vanden-Eijnden
2022	J	`jnl`	A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks. CoRR Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna
2022	J	`jnl`	Building Normalizing Flows with Stochastic Interpolants. CoRR Michael S. Albergo, Eric Vanden-Eijnden
2022	A*	`conf`	Learning Optimal Flows for Non-Equilibrium Importance Sampling. NeurIPS Yu Cao, Eric Vanden-Eijnden
2022	A*	`conf`	Learning sparse features can lead to overfitting in neural networks. NeurIPS Leonardo Petrini, Francesco Cagnetta, Eric Vanden-Eijnden, Matthieu Wyart
2022	J	`jnl`	Learning sparse features can lead to overfitting in neural networks. CoRR Leonardo Petrini, Francesco Cagnetta, Eric Vanden-Eijnden, Matthieu Wyart
2022	J	`jnl`	Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations. CoRR Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden
2022	J	`jnl`	On Feature Learning in Neural Networks with Global Convergence Guarantees. CoRR Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna
2022	A*	`conf`	On feature learning in neural networks with global convergence guarantees. ICLR Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna
2022	J	`jnl`	Probability flow solution of the Fokker-Planck equation. CoRR Nicholas M. Boffi, Eric Vanden-Eijnden
2021	—	`conf`	Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and Generalization. MSML Grant M. Rotskoff, Andrew R. Mitchell, Eric Vanden-Eijnden
2021	J	`jnl`	Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks. CoRR Carles Domingo-Enrich, Alberto Bietti, Marylou Gabrié, Joan Bruna, Eric Vanden-Eijnden
2021	J	`jnl`	Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods. CoRR Marylou Gabrié, Grant M. Rotskoff, Eric Vanden-Eijnden
2021	J	`jnl`	Estimating earthquake-induced tsunami inundation probabilities without sampling. CoRR Shanyin Tong, Eric Vanden-Eijnden, Georg Stadler
2021	A*	`conf`	On Energy-Based Models with Overparametrized Shallow Neural Networks. ICML Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden, Joan Bruna
2021	J	`jnl`	On Energy-Based Models with Overparametrized Shallow Neural Networks. CoRR Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden, Joan Bruna
2020	A*	`conf`	A Dynamical Central Limit Theorem for Shallow Neural Networks. NeurIPS Zhengdao Chen, Grant M. Rotskoff, Joan Bruna, Eric Vanden-Eijnden
2020	J	`jnl`	A Dynamical Central Limit Theorem for Shallow Neural Networks. CoRR Zhengdao Chen, Grant M. Rotskoff, Joan Bruna, Eric Vanden-Eijnden
2020	J	`jnl`	Extreme event probability estimation using PDE-constrained optimization and large deviation theory, with application to tsunamis. CoRR Shanyin Tong, Eric Vanden-Eijnden, Georg Stadler
2020	A*	`conf`	Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions. NeurIPS Stefano Sarao Mannelli, Eric Vanden-Eijnden, Lenka Zdeborová
2020	J	`jnl`	Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions. CoRR Stefano Sarao Mannelli, Eric Vanden-Eijnden, Lenka Zdeborová
2019	J	`jnl`	Extreme Event Quantification in Dynamical Systems with Random Components. SIAM/ASA J. Uncertain. Quantification Giovanni Dematteis, Tobias Grafke, Eric Vanden-Eijnden
2019	J	`jnl`	Global convergence of neuron birth-death dynamics. CoRR Grant M. Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden
2019	A*	`conf`	Neuron birth-death dynamics accelerates gradient descent and converges asymptotically. ICML Grant M. Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden
2018	J	`jnl`	Neural Networks as Interacting Particle Systems: Asymptotic Convexity of the Loss Landscape and Universal Scaling of the Approximation Error. CoRR Grant M. Rotskoff, Eric Vanden-Eijnden
2018	A*	`conf`	Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks. NeurIPS Grant M. Rotskoff, Eric Vanden-Eijnden
2017	J	`jnl`	Metastability of the Nonlinear Wave Equation: Insights from Transition State Theory. J. Nonlinear Sci. Katherine Newhall, Eric Vanden-Eijnden
2016	J	`jnl`	Self-guided Langevin dynamics via generalized Langevin equation. J. Comput. Chem. Xiongwu Wu, Bernard R. Brooks, Eric Vanden-Eijnden
2014	J	`jnl`	Arclength Parametrized Hamilton's Equations for the Calculation of Instantons. Multiscale Model. Simul. Tobias Grafke, Rainer Grauer, T. Schäfer, Eric Vanden-Eijnden
2014	J	`jnl`	Flows in Complex Networks: Theory, Algorithms, and Application to Lennard-Jones Cluster Rearrangement. CoRR Maria Cameron, Eric Vanden-Eijnden
2014	J	`jnl`	Metropolis Integration Schemes for Self-Adjoint Diffusions. Multiscale Model. Simul. Nawaf Bou-Rabee, Aleksandar Donev, Eric Vanden-Eijnden
2012	J	`jnl`	A patch that imparts unconditional stability to explicit integrators for Langevin-like equations. J. Comput. Phys. Nawaf Bou-Rabee, Eric Vanden-Eijnden
2012	J	`jnl`	The heterogeneous multiscale method. Acta Numer. Assyr Abdulle, Weinan E, Björn Engquist, Eric Vanden-Eijnden
2011	J	`jnl`	Diffusion Estimation from Multiscale Data by Operator Eigenpairs. Multiscale Model. Simul. Daan Crommelin, Eric Vanden-Eijnden
2011	J	`jnl`	The String Method as a Dynamical System. J. Nonlinear Sci. Maria Cameron, Robert V. Kohn, Eric Vanden-Eijnden
2010	J	`jnl`	Clustering and Classification through Normalizing Flows in Feature Space. Multiscale Model. Simul. Juan P. Agnelli, M. Cadeiras, Esteban G. Tabak, Cristina Vilma Turner, Eric Vanden-Eijnden
2010	J	`jnl`	Optimal Fuzzy Aggregation of Networks. Multiscale Model. Simul. Marco Sarich, Christof Schütte, Eric Vanden-Eijnden
2009	J	`jnl`	A general strategy for designing seamless multiscale methods. J. Comput. Phys. Weinan E, Weiqing Ren, Eric Vanden-Eijnden
2009	J	`jnl`	Data-Based Inference of Generators for Markov Jump Processes Using Convex Optimization. Multiscale Model. Simul. Daan Crommelin, Eric Vanden-Eijnden
2009	J	`jnl`	Remarks on Drift Estimation for Diffusion Processes. Multiscale Model. Simul. Yvo Pokern, Andrew M. Stuart, Eric Vanden-Eijnden
2009	J	`jnl`	Some recent techniques for free energy calculations. J. Comput. Chem. Eric Vanden-Eijnden
2008	J	`jnl`	Accelerated Simulation of a Heavy Particle in a Gas of Elastic Spheres. Multiscale Model. Simul. Gil Ariel, Eric Vanden-Eijnden
2008	J	`jnl`	Transition Path Theory for Markov Jump Processes. Multiscale Model. Simul. Philipp Metzner, Christof Schütte, Eric Vanden-Eijnden
2007	J	`jnl`	Nested stochastic simulation algorithms for chemical kinetic systems with multiple time scales. J. Comput. Phys. Weinan E, Di Liu, Eric Vanden-Eijnden
2006	J	`jnl`	Fitting timeseries by continuous-time Markov chains: A quadratic programming approach. J. Comput. Phys. Daan Crommelin, Eric Vanden-Eijnden
2005	J	`jnl`	Magnetic Elements at Finite Temperature and Large Deviation Theory. J. Nonlinear Sci. Robert V. Kohn, Maria G. Reznikoff, Eric Vanden-Eijnden