Vanden-Eijnden, Eric

26 publications

ICLR 2026 Generative Modeling from Black-Box Corruptions via Self-Consistent Stochastic Interpolants Chirag Modi, Jiequn Han, Eric Vanden-Eijnden, Joan Bruna

ICLRW 2025 An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control Mengjian Hua, Mathieu Lauriere, Eric Vanden-Eijnden

NeurIPS 2025 Dynamic Test-Time Compute Scaling in Control Policy: Difficulty-Aware Stochastic Interpolant Policy Inkook Chun, Seungjae Lee, Michael Samuel Albergo, Saining Xie, Eric Vanden-Eijnden

NeurIPS 2025 FEAT: Free Energy Estimators with Adaptive Transport Yuanqi Du, Jiajun He, Francisco Vargas, Yuanqing Wang, Carla P Gomes, José Miguel Hernández-Lobato, Eric Vanden-Eijnden

TMLR 2025 Flow mAP Matching with Stochastic Interpolants: A Mathematical Framework for Consistency Models Nicholas Matthew Boffi, Michael Samuel Albergo, Eric Vanden-Eijnden

NeurIPS 2025 How to Build a Consistency Model: Learning Flow Maps via Self-Distillation Nicholas Matthew Boffi, Michael Samuel Albergo, Eric Vanden-Eijnden

NeurIPS 2025 Multitask Learning with Stochastic Interpolants Hugo Negrel, Florentin Coeurdoux, Michael Samuel Albergo, Eric Vanden-Eijnden

ICML 2025 NETS: A Non-Equilibrium Transport Sampler Michael Samuel Albergo, Eric Vanden-Eijnden

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

JMLR 2025 Stochastic Interpolants: A Unifying Framework for Flows and Diffusions Michael Albergo, Nicholas M. Boffi, 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 Zdeborova

ICLR 2024 Multimarginal Generative Modeling with Stochastic Interpolants Michael Samuel Albergo, Nicholas Matthew Boffi, Michael Lindsey, Eric Vanden-Eijnden

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

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

ICML 2024 Stochastic Interpolants with Data-Dependent Couplings Michael Samuel Albergo, Mark Goldstein, Nicholas Matthew Boffi, Rajesh Ranganath, Eric Vanden-Eijnden

ICLR 2023 Building Normalizing Flows with Stochastic Interpolants Michael Samuel Albergo, Eric Vanden-Eijnden

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

NeurIPS 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

ICLR 2022 On Feature Learning in Neural Networks with Global Convergence Guarantees Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna

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

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

NeurIPS 2020 A Dynamical Central Limit Theorem for Shallow Neural Networks Zhengdao Chen, Grant 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

ICML 2019 Neuron Birth-Death Dynamics Accelerates Gradient Descent and Converges Asymptotically Grant Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden

NeurIPS 2018 Parameters as Interacting Particles: Long Time Convergence and Asymptotic Error Scaling of Neural Networks Grant Rotskoff, Eric Vanden-Eijnden