Summary of Smoothness Adaptive Hypothesis Transfer Learning, by Haotian Lin et al.
Smoothness Adaptive Hypothesis Transfer Learning
by Haotian Lin, Matthew Reimherr
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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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 proposes Smoothness Adaptive Transfer Learning (SATL), a two-phase kernel ridge regression (KRR)-based algorithm for hypothesis transfer learning. SATL adapts to the unknown smoothness of target and source functions by employing Gaussian kernels in both phases, allowing it to achieve minimax optimality. The authors prove that misspecified fixed bandwidth Gaussian kernel learning can achieve minimax optimality and derive an adaptive procedure to the unknown Sobolev smoothness. They also show that SATL enjoys a matching upper bound up to a logarithmic factor compared to non-transfer learning settings. Experiments confirm the results, demonstrating the superiority of SATL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can learn from each other’s experiences. It proposes a new way for machines to transfer what they’ve learned from one task to another. This helps them get better at doing things without having to start from scratch. The method uses special math formulas and adjusts its approach based on the complexity of the tasks it’s trying to learn. The results show that this approach is very effective and can help machines make even more accurate predictions. |
Keywords
* Artificial intelligence * Regression * Transfer learning
