Summary of Task-agnostic Machine-learning-assisted Inference, by Jiacheng Miao and Qiongshi Lu
Task-Agnostic Machine-Learning-Assisted Inference
by Jiacheng Miao, Qiongshi Lu
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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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 PSPS framework is a novel statistical approach that enables task-agnostic ML-assisted inference for massive samples. This method leverages predicted outcomes from machine learning models to facilitate downstream statistical inference, allowing researchers to integrate classical statistical approaches with machine learning techniques. By providing a post-prediction inference solution that can be easily plugged into established data analysis routines, PSPS overcomes the limitations of existing methods and enables valid and efficient inference. The framework is demonstrated to be robust to arbitrary choice of ML model and outperforms existing approaches through extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is helping scientists do their jobs faster and better. This paper introduces a new way to use machine learning predictions in statistics, making it possible to analyze big data sets more easily. The method is flexible and can be used with many different statistical tools already available. It’s like having a superpower that helps researchers get accurate results quickly. |
Keywords
* Artificial intelligence * Inference * Machine learning
