Position: Why We Must Rethink Empirical Research in Machine Learning
Authors: Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This position paper warns against similar tendencies in empirical research in machine learning (ML) and calls for a mindset change to address methodological and epistemic challenges of experimentation. |
| Researcher Affiliation | Academia | 1Institute for Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, LMU Munich, Munich, Germany 2Munich Center for Machine Learning (MCML), Munich, Germany 3University of Tübingen, Tübingen, Germany 4Department of Statistics, LMU Munich, Munich, Germany 5Institute of Informatics, LMU Munich, Munich, Germany. |
| Pseudocode | No | The paper is a position paper and does not present any algorithms or pseudocode. |
| Open Source Code | No | The paper is a position paper and does not release its own source code for a new method. |
| Open Datasets | No | The paper is a position paper and does not describe experiments conducted with a specific dataset. |
| Dataset Splits | No | The paper is a position paper and does not describe experiments conducted with dataset splits. |
| Hardware Specification | No | The paper is a position paper and does not conduct experiments, so no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is a position paper and does not conduct experiments, so no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is a position paper and does not conduct experiments, so no experimental setup details or hyperparameters are provided. |