Summary of Accurate Coresets For Latent Variable Models and Regularized Regression, by Sanskar Ranjan and Supratim Shit
Accurate Coresets for Latent Variable Models and Regularized Regression
by Sanskar Ranjan, Supratim Shit
First submitted to arxiv on: 28 Dec 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 introduces a unified framework for constructing accurate coresets, which are weighted subsets of the original dataset that enable models trained on them to maintain the same level of accuracy as those trained on the full dataset. The authors present algorithms for general problems, including latent variable model problems and ℓp-regularized ℓp-regression. For latent variable models, the coreset size is O(poly(k)), where k is the number of latent variables. For ℓp-regularized ℓp-regression, the algorithm captures the reduction in model complexity due to regularization, resulting in a coreset whose size is always smaller than dp for a regularization parameter λ > 0. The authors demonstrate their theoretical findings with extensive experimental evaluations on real datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make machine learning models work better by creating special subsets of data that keep the same level of accuracy as using all the data. They came up with a way to do this for lots of different kinds of problems, including ones where you’re trying to find patterns in big groups of things or ones where you’re trying to get rid of noise. They also showed how their method can make models work better by getting rid of extra information that’s not important. To prove it all works, they tested it on real data and found it made a big difference. |
Keywords
» Artificial intelligence » Machine learning » Regression » Regularization
