Summary of How to Boost Any Loss Function, by Richard Nock and Yishay Mansour
How to Boost Any Loss Function
by Richard Nock, Yishay Mansour
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 Boosting is a successful machine learning optimization setting that learns models based on a weak learner oracle. The goal is to create classifiers that perform better than random guessing. Unlike gradient-based optimization, boosting does not require first-order information about a loss function. Instead, it has evolved into a zeroth-order optimization setting. Recent progress in extending gradient-based optimization to use only zeroth-order information raises questions about which loss functions can be efficiently optimized with boosting and what information is needed for boosting to meet its original requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Boosting is a type of machine learning that helps computers learn from small, good models. The goal is to make better models that are different from random guesses. Boosting doesn’t need information about how well it’s doing, unlike other methods. Recently, people have found ways to use this method with zero information, which makes us wonder what kind of problems can be solved using boosting and what it really needs to work. |
Keywords
» Artificial intelligence » Boosting » Loss function » Machine learning » Optimization
