Summary of Adaptive Data Analysis For Growing Data, by Neil G. Marchant and Benjamin I. P. Rubinstein
Adaptive Data Analysis for Growing Data
by Neil G. Marchant, Benjamin I. P. Rubinstein
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 tackles the challenge of reusing data in adaptive workflows while ensuring against overfitting and maintaining statistical validity. Previous work has shown that interacting with data using differentially private algorithms can mitigate overfitting, but this assumes static data. The paper addresses this gap by presenting the first generalization bounds for adaptive analysis in dynamic data settings. It allows analysts to adaptively schedule queries based on current data size and incorporates time-varying empirical accuracy bounds and mechanisms for tighter guarantees as data accumulates. The asymptotic data requirements of the bound grow with the square-root of the number of adaptive queries, matching prior works’ improvement over data splitting for static settings. This work is instantiated with the clipped Gaussian mechanism, which empirically outperforms baselines composed from static bounds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us reuse data in a special kind of workflow called adaptive analysis. When we use data like this, it’s easy to get stuck in a rut and not make good predictions. The researchers looked at how to solve this problem by using different algorithms that are designed to keep the data safe. They found that if we can adjust our questions based on how much data we have, we can make sure that our results are reliable. This is important because it means we can use more data and get better answers. The researchers tested their idea and found that it worked well. |
Keywords
» Artificial intelligence » Generalization » Overfitting
