Statistical Entropic Optimal Transport: Theory, Methods and Applications — NSF Award to Carnegie Mellon University (PA, $120,737)
Optimal transport provides a sensible mathematical framework to address the fundamental statistical question of how a statistician measures the distance between two distributions based on possibly large high-dimensional datasets. A variation of the original transportation problem featuring an entropic penalization has
| Award title | Statistical Entropic Optimal Transport: Theory, Methods and Applications |
|---|---|
| Award ID | 2412895 |
| Awardee | Carnegie Mellon University |
| City | PITTSBURGH |
| State | PA |
| Amount obligated | $120,737 |
| Principal investigator | Gonzalo Mena |
| Program | STATISTICS |
| Start date | 07/01/2024 |
| Abstract | Optimal transport provides a sensible mathematical framework to address the fundamental statistical question of how a statistician measures the distance between two distributions based on possibly large high-dimensional datasets. A variation of the original transportation problem featuring an entropic penalization has appeared as a more scalable alternative, fueling a wave of new results and successful applications in domains such as genomics, neuroscience, and economics, to name a few. Despite |
| Source | NSF Awards |
$799/mo
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