Summary of The Data Addition Dilemma, by Judy Hanwen Shen et al.
The Data Addition Dilemma
by Judy Hanwen Shen, Inioluwa Deborah Raji, Irene Y. Chen
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, the authors tackle the “Data Addition Dilemma” – the phenomenon where adding more training data to machine learning models can actually hinder their performance. They identify a trade-off between model improvement from increased data and deterioration from distribution shift. To navigate this dilemma, they propose baseline strategies using distribution shift heuristics to guide data source selection, ensuring expected model performance improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a big box of puzzle pieces that don’t quite fit together. That’s kind of like what happens when we add more training data to machine learning models for healthcare. Sometimes it helps, but sometimes it makes things worse! This paper figures out why this is happening and suggests ways to make sure adding more data actually makes our models better. |
Keywords
» Artificial intelligence » Machine learning
