Summary of Transfer Learning Beyond Bounded Density Ratios, by Alkis Kalavasis et al.
Transfer Learning Beyond Bounded Density Ratios
by Alkis Kalavasis, Ilias Zadik, Manolis Zampetakis
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Statistics Theory (math.ST); Machine Learning (stat.ML)
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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 This paper delves into the fundamental problem of transfer learning, where a learning algorithm collects data from one distribution P but needs to perform well on another target distribution Q. The traditional change of measure approach suggests that transfer learning occurs when the density ratio dQ/dP is bounded. However, recent works by Kpotufe and Martinet (COLT, 2018) and Hanneke and Kpotufe (NeurIPS, 2019) demonstrate counterintuitive cases where this ratio is unbounded yet transfer learning remains possible. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transfer learning lets machines learn from one dataset and apply that knowledge to another. The paper looks at when this works and why it’s surprising that sometimes it does even when the two datasets are very different. |
Keywords
* Artificial intelligence * Transfer learning
