mlsanityData to 7 Oct 2026

Data Driven Conditional Optimal Transport

arXiv 1910.11422 ↗First posted 24 Oct 2019math.OCcited by 8

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Abstract (arXiv)

A data driven procedure is developed to compute the optimal map between two conditional probabilities ρ(x|z1,...,zL) and μ(y|z1,...,zL) depending on a set of covariates zi. The procedure is tested on synthetic data from the ACIC Data Analysis Challenge 2017 and it is applied to non uniform lightness transfer between images. Exactly solvable examples and simulations are performed to highlight the differences with ordinary optimal transport.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Oct 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 2; 2026: 1.

8 papers.

  1. 2024Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matchingcited by 34
  2. 2023Consistent Optimal Transport with Empirical Conditional Measurescited by 6
  3. 2025A friendly introduction to triangular transportcited by 4
  4. 2023Y-Diagonal Couplings: Approximating Posteriors with Conditional Wasserstein Distancescited by 3
  5. 2024Quantum Theory and Application of Contextual Optimal Transportcited by 3
  6. 2024Modeling Stochastic Conditional Dynamics from Sparse Observations via Kernel-Stabilized Flow Matchingcited by 3
  7. 2025Invariant Feature Extraction Through Conditional Independence and the Optimal Transport Barycenter Problem: the Gaussian casecited by 0
  8. 2026Approximating Measures on Function Spaces: Transport and Truncationcited by 0