| 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 | 56 | 2015 |
| WCET-driven cache-aware code positioning H Falk, H Kotthaus Proceedings of the 14th international conference on Compilers, architectures …, 2011 | 42 | 2011 |
| 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 | 27 | 2015 |
| 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 | 22 | 2022 |
| 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 | 20 | 2016 |
| 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 | 18 | 2014 |
| 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 | 16 | 2017 |
| 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 | 8 | 2023 |
| 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 | 8 | 2022 |
| Scheduling data-intensive tasks on heterogeneous many cores P Tözün, H Kotthaus {IEEE} Data Engineering Bulletin 42 (1), 61-72, 2019 | 8 | 2019 |
| Methods for efficient resource utilization in statistical machine learning algorithms H Kotthaus | 8 | 2018 |
| 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 | 7 | 2021 |
| 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 | 7 | 2019 |
| 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 | 6 | 2015 |
| 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 | 4 | 2016 |
| 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 | 3 | 2014 |
| 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 | 3 | 2012 |
| 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 | 2 | 2017 |
| SancScreen: Towards a Real-world Dataset for Evaluating Explainability Methods. M Jakobs, H Kotthaus, I Röder, M Baritz LWDA, 33-44, 2022 | 1 | 2022 |
