Julian Gold

2 accepted papers

2025

Hierarchical Refinement: Optimal Transport to Infinity and Beyond

Peter Halmos, Julian Gold, Xinhao Liu, Benjamin Raphael

ICML 2025oral

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 efficiency of the Sinkhorn algorithm (Cuturi, 2013). However, Sinkhorn has quadratic space complexity in the number of point…

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2024

Low-Rank Optimal Transport through Factor Relaxation with Latent Coupling

Peter Halmos, Xinhao Liu, Julian Gold, Benjamin Raphael

NeurIPS 2024poster

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 learning. A key challenge in applying OT to massive datasets is the quadratic scaling of the coupling matrix with the size of…

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