Summary of On the Erm Principle in Meta-learning, by Yannay Alon et al.
On the ERM Principle in Meta-Learning
by Yannay Alon, Steve Hanneke, Shay Moran, Uri Shalit
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers explore the realm of meta-learning, a type of machine learning that involves training across multiple tasks. By leveraging this approach, they aim to develop algorithms capable of adapting to new, unseen problems. The authors propose a novel method for evaluating these algorithms’ performance, dubbed the two-dimensional learning surface, which accounts for varying numbers of tasks and training examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Meta-learning is an exciting area that can help machines learn more efficiently. By training on many different tasks, they can develop skills that allow them to adapt to new situations. This paper introduces a way to measure how well these algorithms perform in real-world scenarios. |
Keywords
* Artificial intelligence * Machine learning * Meta learning
