2 code implementations • NeurIPS 2023 • Neeratyoy Mallik, Edward Bergman, Carl Hvarfner, Danny Stoll, Maciej Janowski, Marius Lindauer, Luigi Nardi, Frank Hutter
Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance.
2 code implementations • NeurIPS 2023 • Simon Schrodi, Danny Stoll, Binxin Ru, Rhea Sukthanker, Thomas Brox, Frank Hutter
In this work, we introduce a unifying search space design framework based on context-free grammars that can naturally and compactly generate expressive hierarchical search spaces that are 100s of orders of magnitude larger than common spaces from the literature.
no code implementations • 16 Jul 2022 • Diane Wagner, Fabio Ferreira, Danny Stoll, Robin Tibor Schirrmeister, Samuel Müller, Frank Hutter
Self-Supervised Learning (SSL) has become a very active area of Deep Learning research where it is heavily used as a pre-training method for classification and other tasks.
1 code implementation • 23 Apr 2022 • Carl Hvarfner, Danny Stoll, Artur Souza, Marius Lindauer, Frank Hutter, Luigi Nardi
To address this issue, we propose $\pi$BO, an acquisition function generalization which incorporates prior beliefs about the location of the optimum in the form of a probability distribution, provided by the user.
no code implementations • 11 Jan 2022 • Zhengying Liu, Adrien Pavao, Zhen Xu, Sergio Escalera, Fabio Ferreira, Isabelle Guyon, Sirui Hong, Frank Hutter, Rongrong Ji, Julio C. S. Jacques Junior, Ge Li, Marius Lindauer, Zhipeng Luo, Meysam Madadi, Thomas Nierhoff, Kangning Niu, Chunguang Pan, Danny Stoll, Sebastien Treguer, Jin Wang, Peng Wang, Chenglin Wu, Youcheng Xiong, Arbe r Zela, Yang Zhang
Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly.
no code implementations • ICLR 2022 • Carl Hvarfner, Danny Stoll, Artur Souza, Luigi Nardi, Marius Lindauer, Frank Hutter
To address this issue, we propose $\pi$BO, an acquisition function generalization which incorporates prior beliefs about the location of the optimum in the form of a probability distribution, provided by the user.
1 code implementation • 25 Oct 2020 • Danny Stoll, Jörg K. H. Franke, Diane Wagner, Simon Selg, Frank Hutter
After developer adjustments to a machine learning (ML) algorithm, how can the results of an old hyperparameter optimization (HPO) automatically be used to speedup a new HPO?
5 code implementations • ICLR 2019 • Frederic Runge, Danny Stoll, Stefan Falkner, Frank Hutter
Designing RNA molecules has garnered recent interest in medicine, synthetic biology, biotechnology and bioinformatics since many functional RNA molecules were shown to be involved in regulatory processes for transcription, epigenetics and translation.