Eric Vanden-Eijnden

Gaussian Flow-Matching Schedules: Implications for Sampling and Training

Arsène Claustre, Hugo Negrel, Claire Boyer +2

Flow-matching schedules affect both sampling dynamics and the variance of the regression target. For centered commuting Gaussians, we show that a direction-dependent schedule decom…

Beckmann Transport Models: From Autonomous Flows to One-Step Maps

Lee Cheuk-Kit, Florentin Coeurdoux, Yuyuan Chen +5

We propose an instantiation of flow matching that relies on a time-independent velocity field (an \emph{autonomous flow}) to exactly map between two distributions, so long as the t…

Covariance Shrinkage via Stochastic Interpolation

Mathieu Chalvidal, Florentin Coeurdoux, Eric Vanden-Eijnden

We recast classical shrinkage of high-dimensional covariance estimators as empirical risk minimization over a parametric stochastic interpolant between a source and a target distri…

Reactive Flux Matching: Mechanism Discovery and Adaptive Sampling of Rare Events

Rishal Aggarwal, David Ryan Koes, Nicholas M. Boffi +1

Path sampling methods generate ensembles of reactive trajectories connecting metastable states, but extracting mechanistic insight from these data remains nontrivial. We introduce…

Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants

Chirag Modi, Jiequn Han, Eric Vanden-Eijnden +1

Transport-based methods have emerged as a leading paradigm for building generative models from large, clean datasets. However, in many scientific and engineering domains, clean dat…

Test-time scaling of diffusions with flow maps

Amirmojtaba Sabour, Michael S. Albergo, Carles Domingo-Enrich +4

A common recipe to improve diffusion models at test-time so that samples score highly against a user-specified reward is to introduce the gradient of the reward into the dynamics o…

Multitask Learning with Stochastic Interpolants

Hugo Negrel, Florentin Coeurdoux, Michael S. Albergo +1

We propose a framework for learning maps between probability distributions that broadly generalizes the time dynamics of flow and diffusion models. To enable this, we generalize st…

Simulation-Free Differential Dynamics through Neural Conservation Laws

Mengjian Hua, Eric Vanden-Eijnden, Ricky T. Q. Chen

We present a novel simulation-free framework for training continuous-time diffusion processes over very general objective functions. Existing methods typically involve either presc…

How to build a consistency model: Learning flow maps via self-distillation

Nicholas M. Boffi, Michael S. Albergo, Eric Vanden-Eijnden

Flow-based generative models achieve state-of-the-art sample quality, but require the expensive solution of a differential equation at inference time. Flow map models, commonly kno…

Optimizing Noise Schedules of Generative Models in High Dimensionss

Santiago Aranguri, Giulio Biroli, Marc Mezard +1

Recent works have shown that diffusion models can undergo phase transitions, the resolution of which is needed for accurately generating samples. This has motivated the use of diff…

An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control

Mengjian Hua, Mathieu Laurière, Eric Vanden-Eijnden

We present a novel on-policy algorithm for solving stochastic optimal control (SOC) problems. By leveraging the Girsanov theorem, our method directly computes on-policy gradients o…

NETS: A Non-Equilibrium Transport Sampler

Michael S. Albergo, Eric Vanden-Eijnden

We propose an algorithm, termed the Non-Equilibrium Transport Sampler (NETS), to sample from unnormalized probability distributions. NETS can be viewed as a variant of annealed imp…

Flow map matching with stochastic interpolants: A mathematical framework for consistency models

Nicholas M. Boffi, Michael S. Albergo, Eric Vanden-Eijnden

Generative models based on dynamical equations such as flows and diffusions offer exceptional sample quality, but require computationally expensive numerical integration during inf…