Summary of Mycroft: Towards Effective and Efficient External Data Augmentation, by Zain Sarwar et al.
MYCROFT: Towards Effective and Efficient External Data Augmentation
by Zain Sarwar, Van Tran, Arjun Nitin Bhagoji, Nick Feamster, Ben Y. Zhao, Supriyo Chakraborty
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 Mycroft, a novel machine learning method, tackles the challenge of limited data availability in model training. When datasets are scarce, model trainers must navigate the complex task of acquiring more data from private entities, often hampered by propriety and privacy concerns. To overcome this hurdle, Mycroft leverages feature space distances and gradient matching to identify small but informative data subsets from each owner, allowing for performance optimization with minimal data exposure. Experimental results across four tasks in two domains demonstrate Mycroft’s rapid convergence to the full-information baseline, showcasing its robustness to noise and ability to rank data owners by utility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mycroft is a new way for machine learning models to work with limited data. Usually, models need lots of data to perform well. But when there’s not enough data, it can be hard and expensive to get more from private sources. Mycroft helps trainers find the most useful parts of each dataset, so they can improve their model without sharing too much data. This makes it easier for people to train high-performance models. |
Keywords
* Artificial intelligence * Machine learning * Optimization
