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Helena Kotthaus
Helena Kotthaus
Verified email at tu-dortmund.de
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Cited by
Cited by
Year
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
572015
WCET-driven cache-aware code positioning
H Falk, H Kotthaus
Proceedings of the 14th international conference on Compilers, architectures …, 2011
412011
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
272015
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
242022
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
202016
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
182014
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
162017
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
92023
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
92022
The care label concept: a certification suite for trustworthy and resource-aware machine learning
K Morik, H Kotthaus, L Heppe, D Heinrich, R Fischer, A Pauly, ...
arXiv preprint arXiv:2106.00512, 2021
82021
Scheduling data-intensive tasks on heterogeneous many cores
P Tözün, H Kotthaus
{IEEE} Data Engineering Bulletin 42 (1), 61-72, 2019
82019
Methods for efficient resource utilization in statistical machine learning algorithms
H Kotthaus
82018
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
72019
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
62015
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
42016
Yes we care!-certification for machine learning methods through the care label framework (2021)
K Morik, H Kotthaus, L Heppe, D Heinrich, R Fischer, S Mücke, A Pauly, ...
arXiv preprint arXiv:2105.10197, 0
4
Performance Analysis for R: Towards a Faster R Interpreter
H Kotthaus, I Korb, M Künne, P Marwedel
Abstract Booklet of the International R User Conference, 2014
32014
A JVM-based Compiler Strategy for the R Language
H Kotthaus, S Plazar, P Marwedel
Technical report for Collaborative Research Center SFB 876 Providing …, 2012
32012
R goes Mobile: Efficient Scheduling for Parallel R Programs on Heterogeneous Embedded Systems
H Kotthaus, A Lang, O Neugebauer, P Marwedel
The R User Conference, useR! 2017 July 4-7 2017 Brussels, Belgium, 74, 2017
22017
SancScreen: Towards a Real-world Dataset for Evaluating Explainability Methods.
M Jakobs, H Kotthaus, I Röder, M Baritz
LWDA, 33-44, 2022
12022
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Articles 1–20