Position: A Call to Action for a Human-Centered AutoML Paradigm
Authors: Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas C Mueller, Frank Hutter, Matthias Feurer, Bernd Bischl
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this position paper, we argue that a key to unlocking Auto ML s full potential lies in addressing the currently underexplored aspect of user interaction with Auto ML systems, including their diverse roles, expectations, and expertise. We envision a more human-centered approach in future Auto ML research, promoting the collaborative design of ML systems that tightly integrates the complementary strengths of human expertise and Auto ML methodologies. |
| Researcher Affiliation | Collaboration | 1Institute of Artificial Intelligence (LUH|AI), Leibniz University Hannover, Germany 2L3S Research Center, Hannover, Germany 3Fraunhofer Institute for Integrated Circuits IIS, Fraunhofer IIS, Nuremberg, Germany 4Ludwig Maximilians-Universit at M unchen, Munich, Germany 5Munich Center for Machine Learning, Munich, Germany 6Microsoft, Redmond, USA 7Albert-Ludwigs-Universit at Freiburg, Freiburg, Germany. |
| Pseudocode | No | The paper is a position paper and does not present any new algorithms or methods in the form of pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper discusses existing open-source Auto ML systems and frameworks (e.g., Auto Gluon, Auto-Sklearn, Optuna) as examples of successful Auto ML, but it does not state that the authors are releasing or providing access to source code for any methodology or arguments presented in this specific position paper. |
| Open Datasets | No | The paper is a position paper and does not describe new experimental work that would involve using specific datasets and providing access information for them. It mentions types of data (e.g., 'gene data') or refers to studies that used certain datasets, but it does not use or provide access to datasets for its own research. |
| Dataset Splits | No | The paper is a position paper and does not describe new experiments or provide specific dataset split information (e.g., percentages, sample counts) for training, validation, or testing. |
| Hardware Specification | No | The paper is a position paper and does not describe any new experiments conducted by the authors. Therefore, there are no hardware specifications mentioned for running experiments. |
| Software Dependencies | No | The paper is a position paper and does not present new experimental results or provide specific ancillary software details with version numbers required for replication. It mentions general software packages like 'Auto Gluon' or 'Optuna' in the context of existing work, but not as dependencies for the authors' own work. |
| Experiment Setup | No | The paper is a position paper and does not describe any new experiments. Therefore, it does not provide specific experimental setup details, such as hyperparameter values, training configurations, or system-level settings. |