Summary of Stabilizing Black-box Model Selection with the Inflated Argmax, by Melissa Adrian et al.
Stabilizing black-box model selection with the inflated argmax
by Melissa Adrian, Jake A. Soloff, Rebecca Willett
First submitted to arxiv on: 23 Oct 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 This paper presents a new approach to stabilizing model selection in machine learning. The current methods, such as LASSO and SINDy, are highly unstable when dealing with noisy or incomplete data. To address this issue, the authors propose a novel method that combines bagging and an inflated argmax operation. This method selects a small collection of models that fit the data well and provides stability guarantees. In other words, if some data points are removed from the training set, the selected models will still overlap with the original selection. The proposed method is illustrated through three case studies: (1) a simulation where strongly correlated covariates make standard LASSO model selection unstable, (2) a Lotka-Volterra model selection problem focused on identifying how competition in an ecosystem affects species’ abundances, and (3) a graph subset selection problem using cell-signaling data from proteomics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps scientists choose the right models for their data. Right now, there are problems with choosing models because they can be affected by just one piece of bad data. The researchers developed a new way to select models that is more stable and works well even if some data points are missing or incorrect. They tested this method in three different situations: studying how species interact in an ecosystem, identifying important genes, and understanding how cells communicate. |
Keywords
» Artificial intelligence » Bagging » Machine learning
