1 citations · 1 across the 5 of their papers we have counts for
5 papers
The Score Hamiltonian: Mapping Diffusion Models to Adiabatic Transport
Peter Halmos, Boris Hanin
We exhibit an exact correspondence between sampling with score-based diffusion models and adiabatic transport of ground states for a family of Schrödinger operators we call Score H…
Transport Clustering: Solving Low-Rank Optimal Transport via Clustering
Henri Schmidt, Peter Halmos, Ben Raphael
Optimal transport (OT) finds a least cost transport plan between two probability distributions using a cost matrix defined on pairs of points. Unlike standard OT, which infers unst…
Implicit Bias of the JKO Scheme
Peter Halmos, Boris Hanin
Wasserstein gradient flow provides a general framework for minimizing an energy functional over the space of probability measures on a Riemannian manifold . Its canonica…
Hierarchical Refinement: Optimal Transport to Infinity and Beyond
Peter Halmos, Julian Gold, Xinhao Liu +1
Optimal transport (OT) has enjoyed great success in machine learning as a principled way to align datasets via a least-cost correspondence, driven in large part by the runtime effi…
Low-Rank Optimal Transport through Factor Relaxation with Latent Coupling
Peter Halmos, Xinhao Liu, Julian Gold +1
Optimal transport (OT) is a general framework for finding a minimum-cost transport plan, or coupling, between probability distributions, and has many applications in machine learni…