Summary of Oops, I Sampled It Again: Reinterpreting Confidence Intervals in Few-shot Learning, by Raphael Lafargue et al.
Oops, I Sampled it Again: Reinterpreting Confidence Intervals in Few-Shot Learning
by Raphael Lafargue, Luke Smith, Franck Vermet, Mathias Löwe, Ian Reid, Vincent Gripon, Jack Valmadre
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper presents a critical analysis of confidence interval (CI) computation in few-shot learning (FSL). The current method, which involves sampling tasks with replacement, is shown to be misleading due to its failure to account for the data itself. A comparative study reveals that this approach leads to notable underestimation of CIs. This observation highlights the need for a reevaluation of how confidence intervals are interpreted in FSL comparative studies. The research demonstrates that paired tests can partially address this issue and explores methods to further reduce CI size by strategically sampling tasks. Additionally, an optimized benchmark is introduced, which can be accessed at https://github.com/RafLaf/FSL-benchmark-again. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how we calculate confidence intervals in a type of learning called few-shot learning. Right now, people are using a method that lets the same data be used multiple times. This makes the confidence intervals not very accurate because it’s considering the random way the data is being chosen rather than the actual data itself. The study shows that this approach underestimates the confidence intervals. This means we need to rethink how we use these confidence intervals and the conclusions we draw from them. The research also shows that using paired tests can help with this problem, and it looks at ways to make the confidence intervals even smaller by choosing the right size of tasks. Finally, a new benchmark is introduced that people can access online. |
Keywords
* Artificial intelligence * Few shot
