Summary of Multifidelity Linear Regression For Scientific Machine Learning From Scarce Data, by Elizabeth Qian et al.
Multifidelity linear regression for scientific machine learning from scarce data
by Elizabeth Qian, Dayoung Kang, Vignesh Sella, Anirban Chaudhuri
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
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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 The proposed paper presents a novel multifidelity training approach for scientific machine learning via linear regression, which exploits the scientific context where data of varying fidelities and costs are available. The authors develop an approximate control variate framework to define new multifidelity Monte Carlo estimators for linear regression models. By utilizing both high-fidelity and lower-fidelity data, the approach achieves similar accuracy to traditional high-fidelity only approaches with significantly reduced high-fidelity data requirements. This is particularly relevant in scientific and engineering settings where generating high-fidelity data can be expensive. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to train machine learning models using limited data. Usually, we need lots of good quality data to train these models, but sometimes this data is hard or expensive to get. The authors develop a method that uses both high-quality and low-quality data to train the model, which can achieve similar results as if it only used the high-quality data, but with much less of it. |
Keywords
* Artificial intelligence * Linear regression * Machine learning
