Summary of On Robust Cross Domain Alignment, by Anish Chakrabarty et al.
On Robust Cross Domain Alignment
by Anish Chakrabarty, Arkaprabha Basu, Swagatam Das
First submitted to arxiv on: 20 Dec 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 A novel approach is presented to improve the Gromov-Wasserstein (GW) distance, a widely used measure of alignment between distributions on distinct spaces. The GW distance has been vulnerable to contamination in its underlying measures, and existing efforts to introduce robustness have relied on techniques inspired by optimal transport (OT). However, these methods are not suitable for the cross-domain alignment problem, which requires specific solutions to tackle diverse applications and contamination regimes. To address this, three novel techniques are proposed to robustify GW and its variants. These methods rely on robust statistics and provide guarantees of metric properties and robustness against contamination. Empirical validation is provided through experiments with real machine learning tasks, demonstrating the superiority of these approaches over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves a measure called Gromov-Wasserstein (GW) distance, which helps match distributions on different spaces. The GW distance has been tricky to work with because it’s sensitive to mistakes in its underlying measures. To fix this, new techniques are introduced that use special statistics to make the GW distance more reliable. These methods also provide guarantees about how well they will work and have been tested on real machine learning problems. |
Keywords
» Artificial intelligence » Alignment » Machine learning
