WCET-driven cache-aware code positioning. - Heiko Falk; Helena Kotthaus (CASES, 2011)

Dynamic page sharing optimization for the R language. - Helena Kotthaus; Ingo Korb; Michael Engel; Peter Marwedel (DLS, 2014)

Faster Model-Based Optimization Through Resource-Aware Scheduling Strategies. - Jakob Richter; Helena Kotthaus; Bernd Bischl; Peter Marwedel; Jörg Rahnenführer; Michel Lang (LION, 2016)

mmapcopy: efficient memory footprint reduction using application knowledge. - Ingo Korb; Helena Kotthaus; Peter Marwedel (SAC, 2016)

RAMBO: Resource-Aware Model-Based Optimization with Scheduling for Heterogeneous Runtimes and a Comparison with Asynchronous Model-Based Optimization. - Helena Kotthaus; Jakob Richter; Andreas Lang 002; Janek Thomas; Bernd Bischl; Peter Marwedel; Jörg Rahnenführer; Michel Lang (LION, 2017)

Methods for efficient resource utilization in statistical machine learning algorithms. - Helena Kotthaus (2018)

Can Flexible Multi-Core Scheduling Help to Execute Machine Learning Algorithms Resource-Efficiently? - Helena Kotthaus; Lea Schönberger; Andreas Lang 002; Jian-Jia Chen; Peter Marwedel (SCOPES, 2019)

Scheduling Data-Intensive Tasks on Heterogeneous Many Cores. - Pinar Tözün; Helena Kotthaus (IEEE Data Eng. Bull., 2019)

Self-Supervised Source Code Annotation from Related Research Papers. - Pierre Haritz; Lukas Pfahler; Thomas Liebig; Helena Kotthaus (ICDM (Workshops), 2021)

The Care Label Concept: A Certification Suite for Trustworthy and Resource-Aware Machine Learning. - Katharina Morik; Helena Kotthaus; Lukas Heppe; Danny Heinrich; Raphael Fischer 001; Andreas Pauly; Nico Piatkowski (CoRR, 2021)

Efficient Memory Footprint Reduction. - Helena Kotthaus; Peter Marwedel (Mach. Learn. under Resour. Constraints Vol. 1 (1), 2022)

Optimization of ML on Modern Multicore Systems. - Helena Kotthaus; Peter Marwedel (Mach. Learn. under Resour. Constraints Vol. 1 (1), 2022)

SancScreen: Towards a Real-world Dataset for Evaluating Explainability Methods. - Matthias Jakobs; Helena Kotthaus; Ines Röder; Maximilian Baritz (LWDA, 2022)

Surrogate Model-Based Explainability Methods for Point Cloud NNs. - Hanxiao Tan; Helena Kotthaus (WACV, 2022)

Yes we care!-Certification for machine learning methods through the care label framework. - Katharina Morik; Helena Kotthaus; Raphael Fischer 001; Sascha Mücke; Matthias Jakobs; Nico Piatkowski; Andreas Pauly; Lukas Heppe; Danny Heinrich (Frontiers Artif. Intell., 2022)

Explainability-Aware One Point Attack for Point Cloud Neural Networks. - Hanxiao Tan; Helena Kotthaus (WACV, 2023)