Summary of On the Efficiency Of Erm in Feature Learning, by Ayoub El Hanchi et al.
On the Efficiency of ERM in Feature Learning
by Ayoub El Hanchi, Chris J. Maddison, Murat A. Erdogdu
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 explores the performance of empirical risk minimization (ERM) on regression problems with square loss, focusing on the simplest instance of feature learning. The model is expected to learn an appropriate feature map and a linear predictor from the data. The authors study the asymptotic quantiles of the excess risk of ERM sequences and show that they coincide with those of the oracle procedure, which knows the optimal feature map, up to a factor of two. A non-asymptotic analysis is also provided, relating the global complexity of the feature set to the size of sublevel sets of feature suboptimality. The authors apply their results to obtain new guarantees on the performance of best subset selection procedures in sparse linear regression under general assumptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well a machine learning model does when it tries to learn from data and figure out what features are important. It’s like trying to find the right puzzle pieces that fit together just right. The researchers want to know if the model can learn these puzzle pieces on its own, or if it needs some extra help. They found that in some cases, the model can do pretty well on its own, but it might not always get it exactly right. This is important because it helps us understand how to make our machine learning models better. |
Keywords
» Artificial intelligence » Feature map » Linear regression » Machine learning » Regression
