20 papers
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…
DASIP: Dynamic Test-Time Compute Scaling for Robot Control with Stochastic Interpolant Policies
Inkook Chun, Seungjae Lee, Michael S. Albergo +2
Diffusion- and flow-based policies deliver state-of-the-art performance on long-horizon robotic manipulation and imitation learning tasks. However, these controllers employ a fixed…
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…
Scale-Adaptive Generative Flows for Multiscale Scientific Data
Yifan Chen, Eric Vanden-Eijnden
Flow-based generative models can face numerical challenges on scientific data with multiscale Fourier spectra, often producing large errors at fine scales. We approach this problem…
Variational Optimality of Föllmer Processes in Generative Diffusions
Yifan Chen, Eric Vanden-Eijnden
We construct and analyze generative diffusions that transport a point mass to a prescribed target distribution over a finite time horizon using the stochastic interpolant framework…