Summary of Position: Why We Must Rethink Empirical Research in Machine Learning, by Moritz Herrmann et al.
Position: Why We Must Rethink Empirical Research in Machine Learning
by Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl
First submitted to arxiv on: 3 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 cautions against a widespread misconception in machine learning experimentation, which can lead to unreliable findings and hinder progress in the field. The authors propose recognizing both the diversity of experimental approaches and their limitations to ensure more reliable results. Specifically, they argue that most current empirical machine learning research is focused on confirming hypotheses rather than exploring new ideas. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper warns about a problem with how scientists do experiments in machine learning. They think that if an experiment doesn’t get the expected result, it’s not worth doing again. This can lead to findings that aren’t reliable and stops progress in the field. The authors suggest that researchers should recognize there are many ways to do experiments and that some things just can’t be known for sure. They say most current research is focused on proving what we already think we know, rather than trying new ideas. |
Keywords
» Artificial intelligence » Machine learning
