Name
Abstract | ||
|---|---|---|
A data-driven procedure is developed to compute the optimal map between two conditional probabilities rho(x vertical bar z(1), ..., z(L)) and mu(y vertical bar z(1), ..., z(L)), known only through samples and depending on a set of covariates z(l). 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s10994-021-06060-0 | MACHINE LEARNING |
Keywords | DocType | Volume |
Optimal transport, Conditional average treatment effect, Uncertainty quantification, Color transfer, Image restoration | Journal | 110 |
Issue | ISSN | Citations |
11-12 | 0885-6125 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Esteban G. Tabak | 1 | 14 | 7.45 |
| Giulio Trigila | 2 | 0 | 1.01 |
| Wenjun Zhao | 3 | 0 | 1.01 |