Name
Abstract | ||
|---|---|---|
We propose a new method for smoothly interpolating probability measures using the geometry of optimal transport. To that end, we reduce this problem to the classical Euclidean setting, allowing us to directly leverage the extensive toolbox of spline interpolation. Unlike previous approaches to measurevalued splines, our interpolated curves (i) have a clear interpretation as governing particle flows, which is natural for applications, and (ii) come with the first approximation guarantees on Wasserstein space. Finally, we demonstrate the broad applicability of our interpolation methodology by fitting surfaces of measures using thin-plate splines. |
Year | Venue | DocType |
|---|---|---|
2021 | 24TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS (AISTATS) | Conference |
Volume | ISSN | Citations |
130 | 2640-3498 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sinho Chewi | 1 | 0 | 3.04 |
| Julien Clancy | 2 | 0 | 0.34 |
| Thibaut Le Gouic | 3 | 0 | 3.04 |
| Philippe Rigollet | 4 | 220 | 19.44 |
| George Stepaniants | 5 | 0 | 0.34 |
| Austin Stromme | 6 | 0 | 1.35 |