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2020 – today
- 2024
[j1]Edward Bergman
, Matthias Feurer, Aron Bahram, Amir Rezaei Balef, Lennart Purucker, Sarah Segel, Marius Lindauer, Frank Hutter, Katharina Eggensperger:
AMLTK: A Modular AutoML Toolkit in Python. J. Open Source Softw. 9(100): 6367 (2024)- [c5]Lukas Wegmeth, Tobias Vente, Lennart Purucker:
Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems. ECIR (1) 2024: 140-156 - [i10]Lukas Wegmeth, Tobias Vente, Lennart Purucker:
Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems. CoRR abs/2401.08444 (2024) - [i9]Edward Bergman, Lennart Purucker, Frank Hutter:
Don't Waste Your Time: Early Stopping Cross-Validation. CoRR abs/2405.03389 (2024) - [i8]Jannis Maier, Felix Möller, Lennart Purucker:
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and Cost. CoRR abs/2408.02280 (2024) - [i7]Sebastian Pineda-Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter, Josif Grabocka:
Dynamic Post-Hoc Neural Ensemblers. CoRR abs/2410.04520 (2024) - [i6]Jaris Küken, Lennart Purucker, Frank Hutter:
Large Language Models Engineer Too Many Simple Features For Tabular Data. CoRR abs/2410.17787 (2024) - [i5]Sebastian Pineda-Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter, Josif Grabocka:
Ensembling Finetuned Language Models for Text Classification. CoRR abs/2410.19889 (2024) - [i4]Tobias Strangmann, Lennart Purucker, Jörg K. H. Franke, Ivo Rapant, Fabio Ferreira, Frank Hutter:
Transfer Learning for Finetuning Large Language Models. CoRR abs/2411.01195 (2024) - 2023
- [c4]Lennart Oswald Purucker, Joeran Beel:
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure. AutoML 2023: 1/1-23 - [c3]Lennart Oswald Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel, Bernd Bischl, Holger H. Hoos:
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML. AutoML 2023: 10/1-34 - [c2]Lukas Wegmeth, Tobias Vente, Lennart Purucker, Joeran Beel:
The Effect of Random Seeds for Data Splitting on Recommendation Accuracy. Perspectives@RecSys 2023 - [i3]Lennart Purucker, Joeran Beel:
Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML. CoRR abs/2307.00285 (2023) - [i2]Lennart Purucker, Joeran Beel:
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure. CoRR abs/2307.00286 (2023) - [i1]Lennart Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel, Bernd Bischl, Holger H. Hoos:
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML. CoRR abs/2307.08364 (2023) - 2022
- [c1]Lennart Purucker, Felix I. Stamm, Florian Lemmerich, Joeran Beel:
Estimating the Pruned Search Space Size of Subgroup Discovery. ICDM 2022: 1155-1160
Coauthor Index
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