Summary of Progressive Entropic Optimal Transport Solvers, by Parnian Kassraie et al.
Progressive Entropic Optimal Transport Solvers
by Parnian Kassraie, Aram-Alexandre Pooladian, Michal Klein, James Thornton, Jonathan Niles-Weed, Marco Cuturi
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed class of entropic optimal transport (EOT) solvers, ProgOT, aims to overcome the challenges of tuning EOT hyperparameters by introducing a new method that estimates both plans and transport maps. This approach leverages time discretization and dynamic OT formulations to optimize computation, outperforming standard solvers and neural network-based methods at large scales. The statistical consistency of ProgOT for estimating optimal transport maps is also demonstrated. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ProgOT is a new way to solve the Kantorovich problem and learn a vector-valued push-forward map. It’s like a super-fast and reliable taxi service that can handle big data problems. By using time and space wisely, ProgOT makes it easier to compute optimal transport maps and plans. This means you can get your work done faster and more accurately than before. |
Keywords
» Artificial intelligence » Neural network
