1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2025
DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning
Sarah Segel, Helena Graf, Edward Bergman +5
Hyperparameter optimization (HPO), as a central paradigm of AutoML, is crucial for leveraging the full potential of machine learning (ML) models; yet its complexity poses challenge…
cs.LG2025
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…
cs.LG2020★ 1 cited
Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]
Joeran Beel, Bryan Tyrell, Edward Bergman +2
Automated per-instance algorithm selection often outperforms single learners. Key to algorithm selection via meta-learning is often the (meta) features, which sometimes though do n…