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ORCIDLennart Purucker
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2020 – today
- 2025
[j2]Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, Frank Hutter:
Accurate predictions on small data with a tabular foundation model. Nat. 637(8044): 319-326 (2025)- 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 - [i11]Lukas Wegmeth, Tobias Vente, Lennart Purucker:
Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems. CoRR abs/2401.08444 (2024) - [i10]Edward Bergman, Lennart Purucker, Frank Hutter:
Don't Waste Your Time: Early Stopping Cross-Validation. CoRR abs/2405.03389 (2024) - [i9]Jannis Maier, Felix Möller, Lennart Purucker:
Hardware Aware Ensemble Selection for Balancing Predictive Accuracy and Cost. CoRR abs/2408.02280 (2024) - [i8]Sebastian Pineda-Arango, Maciej Janowski, Lennart Purucker, Arber Zela, Frank Hutter, Josif Grabocka:
Dynamic Post-Hoc Neural Ensemblers. CoRR abs/2410.04520 (2024) - [i7]Jaris Küken, Lennart Purucker, Frank Hutter:
Large Language Models Engineer Too Many Simple Features For Tabular Data. CoRR abs/2410.17787 (2024) - [i6]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) - [i5]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) - [i4]Jun Ma, Feifei Li, Sumin Kim, Reza Asakereh, Bao-Hiep Le, Dang-Khoa Nguyen-Vu, Alexander Pfefferle, Muxin Wei, Ruochen Gao, Donghang Lyu, Songxiao Yang, Lennart Purucker, Zdravko Marinov, Marius Staring, Haisheng Lu, Thuy Thanh Dao, Xincheng Ye, Zhi Li, Gianluca Brugnara, Philipp Vollmuth, Martha Foltyn-Dumitru, Jaeyoung Cho, Mustafa A. Mahmutoglu, Martin Bendszus, Irada Pflüger, Aditya Rastogi, Dong Ni, Xin Yang, Guang-Quan Zhou, Kaini Wang, Nicholas Heller, Nikolaos Papanikolopoulos, Christopher Weight, Yubing Tong, Jayaram K. Udupa, Patrick J. Cahill, Yaqi Wang, Yifan Zhang, Francisco Contijoch, Elliot R. McVeigh, Xin Ye, Shucheng He, Robert Haase, Thomas Pinetz, Alexander Radbruch, Inga Krause, Erich Kobler, Jian He, Yucheng Tang, Haichun Yang, Yuankai Huo, Gongning Luo, Kaisar Kushibar, Jandos Amankulov, Dias Toleshbayev, Amangeldi Mukhamejan, Jan Egger, Antonio Pepe, Christina Gsaxner, Gijs Luijten, Shohei Fujita, Tomohiro Kikuchi, Benedikt Wiestler, Jan S. Kirschke, Ezequiel de la Rosa, Federico Bolelli, Luca Lumetti, Costantino Grana, Kunpeng Xie, Guomin Wu, Behrus Puladi, Carlos Martín-Isla, Karim Lekadir, Víctor M. Campello, Wei Shao, Wayne Brisbane, Hongxu Jiang, Hao Wei, Wu Yuan, Shuangle Li, Yuyin Zhou, Bo Wang:
Efficient MedSAMs: Segment Anything in Medical Images on Laptop. CoRR abs/2412.16085 (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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