update links

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

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. 
   }