Summary of Generalizability Of Experimental Studies, by Federico Matteucci et al.
Generalizability of experimental studies
by Federico Matteucci, Vadim Arzamasov, Jose Cribeiro-Ramallo, Marco Heyden, Konstantin Ntounas, Klemens Böhm
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST)
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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 The proposed paper formalizes experimental studies in machine learning, introducing a quantifiable notion of generalizability. The authors claim that this mathematical framework enables the estimation of the number of experiments required to achieve generalizability and can identify non-generalizable results. To demonstrate its effectiveness, they apply their approach to two published benchmarks, distinguishing between generalizable and non-generalizable outcomes. Additionally, they provide a Python module for replicating the analysis on other experimental studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to develop a mathematical framework for measuring the generalizability of machine learning experiments. It introduces a new notion of generalizability that can be used to analyze existing studies and estimate the number of experiments needed to achieve generalizability in new studies. The authors apply their approach to two recent benchmarks, identifying both generalizable and non-generalizable results. They also release a Python module for repeating their analysis on other experimental studies. |
Keywords
» Artificial intelligence » Machine learning
