Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML L Purucker, J Beel First Conference on Automated Machine Learning (Late-Breaking Workshop), 2022 | 6 | 2022 |
The effect of random seeds for data splitting on recommendation accuracy L Wegmeth, T Vente, L Purucker, J Beel Perspectives on the Evaluation of Recommender Systems Workshop (PERSPECTIVES …, 2023 | 4 | 2023 |
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure L Purucker, J Beel International Conference on Automated Machine Learning, 1/1-23, 2023 | 3 | 2023 |
Don't Waste Your Time: Early Stopping Cross-Validation E Bergman, L Purucker, F Hutter arXiv preprint arXiv:2405.03389, 2024 | 1 | 2024 |
Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems L Wegmeth, T Vente, L Purucker ECIR 2024, 2024 | | 2024 |
DAFT: Data-Aware Fine-Tuning of Foundation Models for Efficient and Effective Medical Image Segmentation AT Pfefferle, L Purucker, F Hutter CVPR 2024: Segment Anything In Medical Images On Laptop, 2024 | | 2024 |
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML L Purucker, L Schneider, M Anastacio, J Beel, B Bischl, H Hoos International Conference on Automated Machine Learning, 10/1-34, 2023 | | 2023 |
Estimating the Pruned Search Space Size of Subgroup Discovery L Purucker, F Stamm, F Lemmerich, J Beel 2022 IEEE International Conference on Data Mining (ICDM), 1155-1160, 2022 | | 2022 |