Name
Abstract | ||
|---|---|---|
The Optimal transport (OT) problem and its associated Wasserstein distance have recently become a topic of great interest in the machine learning community. However, the underlying optimization problem is known to have two major restrictions: (i) it largely depends on the choice of the cost function and (ii) its sample complexity scales exponentially with the dimension. In this paper, we propose a general formulation of a minimax OT problem that can tackle these restrictions by jointly optimizing the cost matrix and the transport plan, allowing us to define a robust distance between distributions. We propose to use a cutting-set method to solve this general problem and show its links and advantages compared to other existing minimax OT approaches. Additionally, we use this method to define a notion of stability allowing us to select the most robust cost matrix. Finally, we provide an experimental study highlighting the efficiency of our approach. |
Year | Venue | DocType |
|---|---|---|
2020 | ICML | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sofien Dhouib | 1 | 0 | 0.34 |
| Ievgen Redko | 2 | 18 | 6.84 |
| Tanguy Kerdoncuff | 3 | 0 | 1.35 |
| Remi Emonet | 4 | 103 | 8.49 |
| Marc Sebban | 5 | 906 | 61.18 |