Summary of Ecoval: An Efficient Data Valuation Framework For Machine Learning, by Ayush K Tarun et al.
EcoVal: An Efficient Data Valuation Framework for Machine Learning
by Ayush K Tarun, Vikram S Chundawat, Murari Mandal, Hong Ming Tan, Bowei Chen, Mohan Kankanhalli
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper introduces EcoVal, a novel data valuation framework for machine learning models that efficiently estimates the value of data in a practical manner. Building upon Shapley value-based frameworks, EcoVal determines the value of data by evaluating clusters of similar points rather than individual samples. The framework formulates model performance as a production function, enabling the estimation of intrinsic and extrinsic values. A formal proof is provided, demonstrating the accelerated performance of EcoVal. The paper showcases its effectiveness for in-distribution and out-of-sample data, addressing the challenge of efficient data valuation at scale. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EcoVal is a new way to figure out how valuable your data is when you’re building machine learning models. Right now, it’s hard to do this quickly and accurately because current methods require lots of model training. EcoVal changes that by grouping similar data points together and calculating the value of each group. It then uses a special kind of math called production functions to figure out how valuable all your data is in total. The researchers show that EcoVal works well for both regular and new, unseen data. This makes it easier to make smart decisions about your data when building machine learning models. |
Keywords
* Artificial intelligence * Machine learning
