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Nathaniel Lahn

4 accepted papers

2025

Scalable Approximation Algorithms for $p$-Wasserstein Distance and Its Variants

ICML 2025poster

The $p$-Wasserstein distance measures the cost of optimally transporting one distribution to another, where the cost of moving a unit mass from $a$ to $b$ is the $p^{th}$ power of the ground distance $\mathrm{d}(a,b)$ between them. Despite its strong theoretical properties, its use in practice --…

Cited by 0SourcePDFScholar
2023

A Combinatorial Algorithm for Approximating the Optimal Transport in the Parallel and MPC Settings

NeurIPS 2023poster

Optimal Transport is a popular distance metric for measuring similarity between distributions. Exact and approximate combinatorial algorithms for computing the optimal transport distance are hard to parallelize. This has motivated the development of numerical solvers (e.g. Sinkhorn method) that can…

2021

A Faster Maximum Cardinality Matching Algorithm with Applications in Machine Learning

NeurIPS 2021poster

Maximum cardinality bipartite matching is an important graph optimization problem with several applications. For instance, maximum cardinality matching in a $\delta$-disc graph can be used in the computation of the bottleneck matching as well as the $\infty$-Wasserstein and the Lévy-Prokhorov distan…

Cited by 5SourcePDFScholar
2019

A Graph Theoretic Additive Approximation of Optimal Transport

NeurIPS 2019poster

Transportation cost is an attractive similarity measure between probability distributions due to its many useful theoretical properties. However, solving optimal transport exactly can be prohibitively expensive. Therefore, there has been significant effort towards the design of scalable approximati…