Summary of Position: Embracing Negative Results in Machine Learning, by Florian Karl and Lukas Malte Kemeter and Gabriel Dax and Paulina Sierak
Position: Embracing Negative Results in Machine Learning
by Florian Karl, Lukas Malte Kemeter, Gabriel Dax, Paulina Sierak
First submitted to arxiv on: 6 Jun 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 Machine learning publications often prioritize predictive performance on specific problems. However, this focus can lead to inefficiencies in the research community and misguided incentives for researchers. This position paper argues that predictive performance alone is not sufficient to determine the value of a publication. Instead, we propose publishing “negative” results, which can help alleviate these issues and improve scientific output. We highlight the advantages of publishing negative results and provide concrete measures for the machine learning community to adopt this paradigm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning research papers often focus on predicting outcomes. But is that all that matters? This paper says no! It argues that we should also share when our ideas don’t work out, or when something we tried didn’t turn out well. This would help make the research process better and more efficient. By sharing “negative” results, scientists can learn from their mistakes and do better in the future. |
Keywords
» Artificial intelligence » Machine learning
