Summary of Robust Barycenter Estimation Using Semi-unbalanced Neural Optimal Transport, by Milena Gazdieva et al.
Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transport
by Milena Gazdieva, Jaemoo Choi, Alexander Kolesov, Jaewoong Choi, Petr Mokrov, Alexander Korotin
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); 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 paper proposes a novel, scalable approach for estimating the robust continuous barycenter in the presence of outliers and noise. The optimal transport (OT) barycenter problem aims to compute the average of probability distributions with respect to OT discrepancies. However, traditional statistical methods are hindered by these issues. The proposed method is framed as a min-max optimization problem, adaptable to general cost functions, and robust to outliers and class imbalance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops an algorithm for estimating the robust barycenter in a continuous distribution setup. It’s the first attempt to do so. The approach is based on the dual formulation of the semi-unbalanced OT problem. The method is tested through illustrative experiments, demonstrating its effectiveness. |
Keywords
» Artificial intelligence » Optimization » Probability
