Summary of Harnessing the Power Of Vicinity-informed Analysis For Classification Under Covariate Shift, by Mitsuhiro Fujikawa et al.
Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift
by Mitsuhiro Fujikawa, Yohei Akimoto, Jun Sakuma, Kazuto Fukuchi
First submitted to arxiv on: 27 May 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 novel dissimilarity measure proposed in this paper leverages vicinity information to analyze excess error in classification under covariate shift, a transfer learning setting where marginal feature distributions differ but conditional label distributions remain the same. The method characterizes excess error using the proposed measure and achieves faster or competitive convergence rates compared to previous techniques. Notably, the approach is effective even when the support non-containment assumption holds, which often appears in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn from experiences by sharing knowledge between different situations. It creates a new way to measure how well a model generalizes to new data, even if the characteristics of that data are very different from what it was trained on. The method is shown to be effective in many scenarios and can help improve transfer learning algorithms. |
Keywords
» Artificial intelligence » Classification » Transfer learning
