Summary of Computational-statistical Gaps in Gaussian Single-index Models, by Alex Damian et al.
Computational-Statistical Gaps in Gaussian Single-Index Models
by Alex Damian, Loucas Pillaud-Vivien, Jason D. Lee, Joan Bruna
First submitted to arxiv on: 8 Mar 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 This paper focuses on Single-Index Models, which are high-dimensional regression problems with a planted structure. Labels depend on an unknown one-dimensional projection of the input through a generic, non-linear transformation. This type of model encompasses various statistical inference tasks and allows researchers to study trade-offs between statistical and computational methods in high-dimensional settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about a type of mathematical problem that tries to figure out how some unknown information affects a set of data points. The key challenge is that the relationship between the data and the unknown information is complex and not always straightforward. By studying these Single-Index Models, researchers can gain insights into how different methods perform in this high-dimensional regime. |
Keywords
* Artificial intelligence * Inference * Regression
