Meta optimal transport
We study the use of amortized optimization to predict optimal transport (OT) maps from the
input measures, which we call Meta OT. This helps repeatedly solve similar OT problems …
input measures, which we call Meta OT. This helps repeatedly solve similar OT problems …
Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey
Optimal Transport (OT) is a mathematical framework that first emerged in the eighteenth
century and has led to a plethora of methods for answering many theoretical and applied …
century and has led to a plethora of methods for answering many theoretical and applied …
On assignment problems related to Gromov–Wasserstein distances on the real line
Let and,, be real numbers. We show by an example that the assignment problem is in
general neither solved by the identical permutation () nor the anti-identical permutation () if …
general neither solved by the identical permutation () nor the anti-identical permutation () if …
Leveraging Optimal Transport via Projections on Subspaces for Machine Learning Applications
C Bonet - arXiv preprint arXiv:2311.13883, 2023 - arxiv.org
Optimal Transport has received much attention in Machine Learning as it allows to compare
probability distributions by exploiting the geometry of the underlying space. However, in its …
probability distributions by exploiting the geometry of the underlying space. However, in its …
Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein
We present a versatile adaptation of existing dimensionality reduction (DR) objectives,
enabling the simultaneous reduction of both sample and feature sizes. Correspondances …
enabling the simultaneous reduction of both sample and feature sizes. Correspondances …