Summary of Dna-se: Towards Deep Neural-nets Assisted Semiparametric Estimation, by Qinshuo Liu et al.
DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation
by Qinshuo Liu, Zixin Wang, Xi-An Li, Xinyao Ji, Lei Zhang, Lin Liu, Zhonghua Liu
First submitted to arxiv on: 4 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: 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 framework for semiparametric estimation formulates the problem as a bi-level optimization problem and develops a scalable algorithm called Deep Neural-Nets Assisted Semiparametric Estimation (DNA-SE) to streamline semiparametric procedures. DNA-SE leverages the universal approximation property of Deep Neural-Nets (DNN) to numerically solve integral equations, outperforming traditional methods in both numerical and statistical aspects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to solve problems in statistics called semiparametric estimation. It uses a special type of computer program called Deep Neural-Nets (DNN) to help find the best solution. The new method is faster and more accurate than old ways of doing it, and can be used for lots of different types of problems. |
Keywords
» Artificial intelligence » Optimization
