Dynamic Neural Optimal Transport for Subset Alignment

Key points

• The goal is to map the source distribution to a reweighted target distribution whose density ratio with respect to the full target lies in $[0, c]$.1
• We propose a dynamic neural optimal transport framework for this setting using two neural networks.1
• Unlike prior neural dynamic optimal transport methods, which typically assume a fixed terminal distribution, our formulation learns the terminal distribution within the support of the target.1
• We demonstrate the method on positive-unlabeled learning, unpaired domain translation, and trajectory inference, where meaningful alignments exist between source and target distributions, but direct distribution matching is inappropriate because the target contains additional or irrelevant components1

Sources (1)

• [1]Dynamic Neural Optimal Transport for Subset Alignment
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 09:32 PM

  We study optimal transport between distributions when only a subset of the target population is relevant to the source.

  The goal is to map the source distribution to a reweighted target distribution whose density ratio with respect to the full target lies in $[0, c]$.

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