40 citations · 160 across the 39 of their papers we have counts for
5 papers · 1 filter
Multimarginal generative modeling with stochastic interpolants
Michael S. Albergo, Nicholas M. Boffi, Michael Lindsey +1
Given a set of probability densities, we consider the multimarginal generative modeling problem of learning a joint distribution that recovers these densities as marginals. The…
Stochastic interpolants with data-dependent couplings
Michael S. Albergo, Mark Goldstein, Nicholas M. Boffi +2
Generative models inspired by dynamical transport of measure -- such as flows and diffusions -- construct a continuous-time map between two probability densities. Conventionally, o…
Deep learning probability flows and entropy production rates in active matter
Nicholas M. Boffi, Eric Vanden-Eijnden
Active matter systems, from self-propelled colloids to motile bacteria, are characterized by the conversion of free energy into useful work at the microscopic scale. They involve p…
Agile Catching with Whole-Body MPC and Blackbox Policy Learning
Saminda Abeyruwan, Alex Bewley, Nicholas M. Boffi +9
We address a benchmark task in agile robotics: catching objects thrown at high-speed. This is a challenging task that involves tracking, intercepting, and cradling a thrown object…
Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden
A class of generative models that unifies flow-based and diffusion-based methods is introduced. These models extend the framework proposed in Albergo and Vanden-Eijnden (2023), ena…