From ea6f89e2381513358181ef1468d33dc21d0bc6d9 Mon Sep 17 00:00:00 2001 From: Brandon Amos Date: Tue, 4 Jun 2024 13:28:58 -0400 Subject: [PATCH] update links --- cv.yaml | 3 +++ publications/all.bib | 3 ++- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/cv.yaml b/cv.yaml index 0e20eab..53c20c3 100644 --- a/cv.yaml +++ b/cv.yaml @@ -385,6 +385,9 @@ repos: - repo_url: https://github.com/facebookresearch/advprompter year: 2024 desc: Fast Adaptive Adversarial Prompting for LLMs + - repo_url: https://github.com/facebookresearch/lagrangian-ot + year: 2024 + desc: Lagrangian OT - repo_url: https://github.com/bamos/zsh-autosuggestions.ai year: 2024 desc: AI-generated autosuggestions for zsh diff --git a/publications/all.bib b/publications/all.bib index 06e5e2b..4a9a32f 100644 --- a/publications/all.bib +++ b/publications/all.bib @@ -19,7 +19,8 @@ @misc{pooladian2023neural year = {2024}, _venue={UAI}, selected={true}, - url={https://openreview.net/forum?id=myb0FKB8C9}, + url={https://arxiv.org/abs/2406.00288}, + codeurl={https://github.com/facebookresearch/lagrangian-ot}, abstract={ We investigate the optimal transport problem between probability measures when the underlying cost function is understood to satisfy a least action principle, also known as a Lagrangian cost. These generalizations are useful when connecting observations from a physical system, where the transport dynamics are influenced by the geometry of the system, such as obstacles, (e.g., incorporating barrier functions in the Lagrangian) and allows practitioners to incorporate a priori knowledge of the underlying system such as non-Euclidean geometries (e.g., paths must be circular). Our contributions are of computational interest, where we demonstrate the ability to efficiently compute geodesics and amortize spline-based paths, which has not been done before, even in low dimensional problems. Unlike prior work, we also output the resulting Lagrangian optimal transport map without requiring an ODE solver. We demonstrate the effectiveness of our formulation on low-dimensional examples taken from prior work. }