8 citations · 9 across the 3 of their papers we have counts for
3 papers
carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks
Carolin Benjamins, Helena Graf, Sarah Segel +14
Hyperparameter Optimization (HPO) is crucial to develop well-performing machine learning models. In order to ease prototyping and benchmarking of HPO methods, we propose carps, a b…
AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks
Alexander Tornede, Difan Deng, Theresa Eimer +8
The fields of both Natural Language Processing (NLP) and Automated Machine Learning (AutoML) have achieved remarkable results over the past years. In NLP, especially Large Language…
Towards Meta-learned Algorithm Selection using Implicit Fidelity Information
Aditya Mohan, Tim Ruhkopf, Marius Lindauer
Automatically selecting the best performing algorithm for a given dataset or ranking multiple algorithms by their expected performance supports users in developing new machine lear…