Summary of Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-statistical Gaps, by Jonathan Kelner et al.
Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps
by Jonathan Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computational Complexity (cs.CC); Data Structures and Algorithms (cs.DS); 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 investigates the performance of Lasso, a popular machine learning algorithm, when covariates have strong correlations. The authors find that Lasso’s prediction error can be much worse than alternative methods like Best Subset Selection, even with significant computational resources. The study suggests that this limitation may be inherent to the problem of sparse linear regression due to a conjectured tradeoff between statistical and computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Lasso is an important tool in machine learning that helps us find patterns in data. But what if our data has related features? This paper looks at how well Lasso works when there are strong connections between these features. They discovered that Lasso can actually perform worse than other methods even with lots of computer power. The researchers think this might be because it’s just hard to balance the need for accurate predictions and efficient computation in certain situations. |
Keywords
* Artificial intelligence * Linear regression * Machine learning
