Articles with public access mandates - Helena KotthausLearn more
Not available anywhere: 3
Runtime and memory consumption analyses for machine learning R programs
H Kotthaus, I Korb, M Lang, B Bischl, J Rahnenführer, P Marwedel
Journal of Statistical Computation and Simulation 85 (1), 14-29, 2015
Mandates: German Research Foundation
Can Flexible Multi-Core Scheduling Help to Execute Machine Learning Algorithms Resource-Efficiently?
H Kotthaus, L Schönberger, A Lang, JJ Chen, P Marwedel
Proceedings of the 22nd International Workshop on Software and Compilers for …, 2019
Mandates: German Research Foundation
Self-Supervised Source Code Annotation from Related Research Papers
P Haritz, L Pfahler, T Liebig, H Kotthaus
2021 International Conference on Data Mining Workshops (ICDMW), 1083-1084, 2021
Mandates: German Research Foundation, Federal Ministry of Education and Research, Germany
Available somewhere: 12
Automatic model selection for high-dimensional survival analysis
M Lang, H Kotthaus, P Marwedel, C Weihs, J Rahnenführer, B Bischl
Journal of Statistical Computation and Simulation 85 (1), 62-76, 2015
Mandates: German Research Foundation
Faster model-based optimization through resource-aware scheduling strategies
J Richter, H Kotthaus, B Bischl, P Marwedel, J Rahnenführer, M Lang
Learning and Intelligent Optimization: 10th International Conference, LION …, 2016
Mandates: German Research Foundation
Surrogate model-based explainability methods for point cloud nns
H Tan, H Kotthaus
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022
Mandates: Federal Ministry of Education and Research, Germany
Dynamic page sharing optimization for the R language
H Kotthaus, I Korb, M Engel, P Marwedel
Proceedings of the 10th ACM Symposium on Dynamic languages, 79-90, 2014
Mandates: German Research Foundation
RAMBO: Resource-aware model-based optimization with scheduling for heterogeneous runtimes and a comparison with asynchronous model-based optimization
H Kotthaus, J Richter, A Lang, J Thomas, B Bischl, P Marwedel, ...
Learning and Intelligent Optimization: 11th International Conference, LION …, 2017
Mandates: German Research Foundation
Explainability-aware one point attack for point cloud neural networks
H Tan, H Kotthaus
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023
Mandates: Federal Ministry of Education and Research, Germany
Yes we care!-Certification for machine learning methods through the care label framework
KJ Morik, H Kotthaus, R Fischer, S Mücke, M Jakobs, N Piatkowski, ...
Frontiers in Artificial Intelligence 5, 975029, 2022
Mandates: German Research Foundation, Federal Ministry of Education and Research, Germany
Scheduling data-intensive tasks on heterogeneous many cores
P Tözün, H Kotthaus
{IEEE} Data Engineering Bulletin 42 (1), 61-72, 2019
Mandates: German Research Foundation
Methods for efficient resource utilization in statistical machine learning algorithms
H Kotthaus
Mandates: German Research Foundation
Performance analysis for parallel R programs: towards efficient resource utilization
H Kotthaus, I Korb, P Marwedel
Abstract Booklet of the International R User Conference (UseR, 66, 2015
Mandates: German Research Foundation
mmapcopy: efficient memory footprint reduction using application knowledge
I Korb, H Kotthaus, P Marwedel
Proceedings of the 31st Annual ACM Symposium on Applied Computing, 1832-1837, 2016
Mandates: German Research Foundation
SancScreen: Towards a Real-world Dataset for Evaluating Explainability Methods.
M Jakobs, H Kotthaus, I Röder, M Baritz
LWDA, 33-44, 2022
Mandates: Federal Ministry of Education and Research, Germany
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