Purucker, Lennart; Tschalzev, Andrej; Erickson, Nick; Blayer, Gioia; Holzmüller, David; Arazi, Alan; Pfefferle, Alexander; Tajjar, Mustafa; Varoquaux, Gaël; Hutter, Frank Beyond IID: How General Are Tabular Foundation Models, Really? Proceedings Article In: Preprint, 2026. @inproceedings{Purucker2026,
title = {Beyond IID: How General Are Tabular Foundation Models, Really?},
author = {Lennart Purucker and Andrej Tschalzev and Nick Erickson and Gioia Blayer and David Holzmüller and Alan Arazi and Alexander Pfefferle and Mustafa Tajjar and Gaël Varoquaux and Frank Hutter
},
year = {2026},
booktitle = {Preprint},
keywords = {}
}
|
Pfefferle, Alexander; Purucker, Lennart; Hutter, Frank DAFT: Data-Aware Fine-Tuning of Foundation Models for Efficient and Effective Medical Image Segmentation Proceedings Article In: Ma, Jun; Zhou, Yuyin; Wang, Bo (Ed.): Medical Image Segmentation Foundation Models. CVPR 2024 Challenge: Segment Anything in Medical Images on Laptop, pp. 15–38, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-81854-7. @inproceedings{pfefferle2025daft,
title = {DAFT: Data-Aware Fine-Tuning of Foundation Models for Efficient and Effective Medical Image Segmentation},
author = {Alexander Pfefferle and Lennart Purucker and Frank Hutter},
editor = {Jun Ma and Yuyin Zhou and Bo Wang},
doi = {10.1007/978-3-031-81854-7_2},
year = {2025},
isbn = {978-3-031-81854-7},
booktitle = {Medical Image Segmentation Foundation Models. CVPR 2024 Challenge: Segment Anything in Medical Images on Laptop},
pages = {15--38},
publisher = {Springer Nature Switzerland},
address = {Cham},
keywords = {}
}
|
Ma, Jun; Li, Feifei; Kim, Sumin; Asakereh, Reza; Le, Bao-Hiep; Nguyen-Vu, Dang-Khoa; Pfefferle, Alexander; Wei, Muxin; Gao, Ruochen; Lyu, Donghang; Yang, Songxiao; Purucker, Lennart; Marinov, Zdravko; Staring, Marius; Lu, Haisheng; Dao, Thuy Thanh; Ye, Xincheng; Li, Zhi; Brugnara, Gianluca; Vollmuth, Philipp; Foltyn-Dumitru, Martha; Cho, Jaeyoung; Mahmutoglu, Mustafa Ahmed; Bendszus, Martin; Pflüger, Irada; Rastogi, Aditya; Ni, Dong; Yang, Xin; Zhou, Guang-Quan; Wang, Kaini; Heller, Nicholas; Papanikolopoulos, Nikolaos; Weight, Christopher; Tong, Yubing; Udupa, Jayaram K; Patrick, Cahill J.; Wang, Yaqi; Zhang, Yifan; Contijoch, Francisco; McVeigh, Elliot; Ye, Xin; He, Shucheng; Haase, Robert; Pinetz, Thomas; Radbruch, Alexander; Krause, Inga; Kobler, Erich; He, Jian; Tang, Yucheng; Yang, Haichun; Huo, Yuankai; Luo, Gongning; Kushibar, Kaisar; Amankulov, Jandos; Toleshbayev, Dias; Mukhamejan, Amangeldi; Egger, Jan; Pepe, Antonio; Gsaxner, Christina; Luijten, Gijs; Fujita, Shohei; Kikuchi, Tomohiro; Wiestler, Benedikt; Kirschke, Jan S.; Rosa, Ezequiel; Bolelli, Federico; Lumetti, Luca; Grana, Costantino; Xie, Kunpeng; Wu, Guomin; Puladi, Behrus; Martín-Isla, Carlos; Lekadir, Karim; Campello, Victor M.; Shao, Wei; Brisbane, Wayne; Jiang, Hongxu; Wei, Hao; Yuan, Wu; Li, Shuangle; Zhou, Yuyin; Wang, Bo Efficient MedSAMs: Segment Anything in Medical Images on Laptop Proceedings 2024. @proceedings{ma2024efficientmedsamssegmentmedical,
title = {Efficient MedSAMs: Segment Anything in Medical Images on Laptop},
author = {Jun Ma and Feifei Li and Sumin Kim and Reza Asakereh and Bao-Hiep Le and Dang-Khoa Nguyen-Vu and Alexander Pfefferle and Muxin Wei and Ruochen Gao and Donghang Lyu and Songxiao Yang and Lennart Purucker and Zdravko Marinov and Marius Staring and Haisheng Lu and Thuy Thanh Dao and Xincheng Ye and Zhi Li and Gianluca Brugnara and Philipp Vollmuth and Martha Foltyn-Dumitru and Jaeyoung Cho and Mustafa Ahmed Mahmutoglu and Martin Bendszus and Irada Pflüger and Aditya Rastogi and Dong Ni and Xin Yang and Guang-Quan Zhou and Kaini Wang and Nicholas Heller and Nikolaos Papanikolopoulos and Christopher Weight and Yubing Tong and Jayaram K Udupa and Cahill J. Patrick and Yaqi Wang and Yifan Zhang and Francisco Contijoch and Elliot McVeigh and Xin Ye and Shucheng He and Robert Haase and Thomas Pinetz and Alexander Radbruch and Inga Krause and Erich Kobler and Jian He and Yucheng Tang and Haichun Yang and Yuankai Huo and Gongning Luo and Kaisar Kushibar and Jandos Amankulov and Dias Toleshbayev and Amangeldi Mukhamejan and Jan Egger and Antonio Pepe and Christina Gsaxner and Gijs Luijten and Shohei Fujita and Tomohiro Kikuchi and Benedikt Wiestler and Jan S. Kirschke and Ezequiel Rosa and Federico Bolelli and Luca Lumetti and Costantino Grana and Kunpeng Xie and Guomin Wu and Behrus Puladi and Carlos Martín-Isla and Karim Lekadir and Victor M. Campello and Wei Shao and Wayne Brisbane and Hongxu Jiang and Hao Wei and Wu Yuan and Shuangle Li and Yuyin Zhou and Bo Wang},
year = {2024},
keywords = {}
}
|
Roberts, Nicholas; Guo, Samuel; Xu, Cong; Talwalkar, Ameet; Lander, David; Tao, Lvfang; Cai, Linhang; Niu, Shuaicheng; Heng, Jianyu; Qin, Hongyang; Deng, Minwen; Hog, Johannes; Pfefferle, Alexander; Shivakumar, Sushil Ammanaghatta; Krishnakumar, Arjun; Wang, Yubo; Sukthanker, Rhea; Hutter, Frank; Hasanaj, Euxhen; Le, Tien-Dung; Khodak, Mikhail; Nevmyvaka, Yuriy; Rasul, Kashif; Sala, Frederic; Schneider, Anderson; Shen, Junhong; Sparks, Evan AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale Proceedings Article In: Ciccone, Marco; Stolovitzky, Gustavo; Albrecht, Jacob (Ed.): Proceedings of the NeurIPS 2022 Competitions Track, pp. 151–170, PMLR, 2022. @inproceedings{pmlr-v220-roberts23a,
title = {AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale},
author = {Nicholas Roberts and Samuel Guo and Cong Xu and Ameet Talwalkar and David Lander and Lvfang Tao and Linhang Cai and Shuaicheng Niu and Jianyu Heng and Hongyang Qin and Minwen Deng and Johannes Hog and Alexander Pfefferle and Sushil Ammanaghatta Shivakumar and Arjun Krishnakumar and Yubo Wang and Rhea Sukthanker and Frank Hutter and Euxhen Hasanaj and Tien-Dung Le and Mikhail Khodak and Yuriy Nevmyvaka and Kashif Rasul and Frederic Sala and Anderson Schneider and Junhong Shen and Evan Sparks},
editor = {Marco Ciccone and Gustavo Stolovitzky and Jacob Albrecht},
year = {2022},
booktitle = {Proceedings of the NeurIPS 2022 Competitions Track},
volume = {220},
pages = {151--170},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
keywords = {}
}
|
