Summary of On the Complexity Of Learning Sparse Functions with Statistical and Gradient Queries, by Nirmit Joshi et al.
On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries
by Nirmit Joshi, Theodor Misiakiewicz, Nathan Srebro
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS)
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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 A novel paper investigates the complexity of gradient algorithms when learning sparse functions, introducing Differentiable Learning Queries (DLQ) to model gradient queries on a specified loss. The study provides a tight characterization of the query complexity of DLQ for learning the support of a sparse function over generic product distributions. This complexity crucially depends on the loss function. For squared loss, DLQ matches Correlation Statistical Queries’ (CSQ) complexity, potentially worse than SQ. However, for other simple loss functions like l1 loss, DLQ achieves the same complexity as SQ. The paper also demonstrates that DLQ can capture learning with stochastic gradient descent by describing the complexity of learning with a two-layer neural network in the mean field regime and linear scaling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well certain algorithms do when they’re trying to learn simple patterns in big data sets. It’s all about finding the right way to use these algorithms so they can learn what we want them to learn, like identifying which features are most important. The researchers came up with a new way to think about this called Differentiable Learning Queries (DLQ). They showed that DLQ is good at figuring out how to use simple loss functions, and it’s even better than some other methods in certain cases. |
Keywords
» Artificial intelligence » Loss function » Neural network » Stochastic gradient descent
