Summary of Theoretical Limitations Of Ensembles in the Age Of Overparameterization, by Niclas Dern et al.
Theoretical Limitations of Ensembles in the Age of Overparameterization
by Niclas Dern, John P. Cunningham, Geoff Pleiss
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 investigates the properties of modern neural network ensembles compared to classic decision tree ensembles. Contrary to previous findings, it is shown that overparameterized neural network ensembles do not inherently provide a generalization advantage over single larger networks. The study uses random feature regressors as a basis for developing theory and proves that infinite ensembles of overparameterized RF regressors become equivalent to single infinite-width RF regressors, exhibiting nearly identical generalization. This finding challenges common assumptions about the advantages of ensembling in overparameterized settings, suggesting a reconsideration of how intuitions from underparameterized ensembles transfer to deep ensembles. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at something called neural network ensembles and compares them to another way of doing things, called decision tree ensembles. The researchers found that these new ensembles don’t really help much when it comes to making predictions. They used a special kind of math to show that the big networks are basically just like the small ones when it comes to how well they do their job. This means we need to rethink what we think about how these ensembles work. |
Keywords
» Artificial intelligence » Decision tree » Generalization » Neural network
