Search Results for author:
Found 6 papers, 3 papers with code
Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks
Experimental results demonstrate the effectiveness of our approach, resulting in models with over 80\% in accuracy and low computational cost.
Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning
In an experimental study targeting the environmental impact of ML, we demonstrate that our approach leads to substantially better Pareto fronts compared to optimizing based on a wrong indicator pre-selected by the user, and performs comparable in the case of an advanced user knowing which indicator to pick.
AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks
The fields of both Natural Language Processing (NLP) and Automated Machine Learning (AutoML) have achieved remarkable results over the past years.
PyExperimenter: Easily distribute experiments and track results
PyExperimenter is a tool to facilitate the setup, documentation, execution, and subsequent evaluation of results from an empirical study of algorithms and in particular is designed to reduce the involved manual effort significantly.
Towards Green Automated Machine Learning: Status Quo and Future Directions
Therefore, we first elaborate on how to quantify the environmental footprint of an AutoML tool.
Algorithm Selection on a Meta Level
The problem of selecting an algorithm that appears most suitable for a specific instance of an algorithmic problem class, such as the Boolean satisfiability problem, is called instance-specific algorithm selection.
