| Benchmarking in optimization: Best practice and open issues T Bartz-Beielstein, C Doerr, D Berg, J Bossek, S Chandrasekaran, ... arXiv preprint arXiv:2007.03488, 2020 | 125 | 2020 |
| Data preprocessing: A new algorithm for univariate imputation designed specifically for industrial needs S Chandrasekaran, M Zaefferer, S Moritz, J Stork, M Friese, A Fischbach, ... | 16 | 2016 |
| Benchmarking in optimization: best practice and open issues. CoRR abs/2007.03488 (2020) T Bartz-Beielstein, C Doerr, J Bossek, S Chandrasekaran, T Eftimov, ... arXiv preprint arXiv:2007.03488, 2020 | 11 | 2020 |
| GECCO Industrial Challenge 2018 Dataset: A water quality dataset for the ‘Internet of Things: Online Anomaly Detection for Drinking Water Quality’competition at the Genetic and … S Moritz, F Rehbach, S Chandrasekaran, M Rebolledo, T Bartz-Beielstein Kyoto, Japan, 2018 | 11 | 2018 |
| GECCO 2018 Industrial Challenge: Monitoring of drinking-water quality F Rehbach, S Moritz, S Chandrasekaran, M Rebolledo, M Friese, ... Accessed: Feb 19, 2019, 2018 | 8 | 2018 |
| NLP based anomaly detection for categorical time series M Horak, S Chandrasekaran, G Tobar 2022 IEEE 23rd International Conference on Information Reuse and Integration …, 2022 | 5 | 2022 |
| Gecco 2017 industrial challenge: Monitoring of drinking water quality S Chandrasekaran, M Freise, J Stork, M Rebolledo, T Bartz-Beielstein | 5 | 2017 |
| Benchmarking in optimization: Best practice and open issues. NY, USA: Cornell University; 2020. 50 p T Bartz-Beielstein, C Doerr, J Bossek, S Chandrasekaran, T Eftimov, ... | 5 | |
| Benchmarking in optimization: Best practice and open issues. arXiv 2020 T Bartz-Beielstein, C Doerr, D Berg, J Bossek, S Chandrasekaran, ... arXiv preprint arXiv:2007.03488, 0 | 4 | |
| Case study II: tuning of gradient boosting (xgboost) T Bartz-Beielstein, S Chandrasekaran, F Rehbach Hyperparameter Tuning for Machine and Deep Learning with R: A Practical …, 2023 | 3 | 2023 |
| Case study iii: Tuning of deep neural networks T Bartz-Beielstein, S Chandrasekaran, F Rehbach Hyperparameter Tuning for Machine and Deep Learning with R: A Practical …, 2023 | 3 | 2023 |
| Technical Report: Flushing Strategies in Drinking Water Systems M Rebolledo, S Chandrasekaran, T Bartz-Beielstein arXiv preprint arXiv:2012.13574, 2020 | 3 | 2020 |
| Ranking and result aggregation T Bartz-Beielstein, O Mersmann, S Chandrasekaran Hyperparameter Tuning for Machine and Deep Learning with R: A Practical …, 2023 | 2 | 2023 |
| EventDetectR--An Open-Source Event Detection System S Chandrasekaran, M Rebolledo, T Bartz-Beielstein arXiv preprint arXiv:2011.09833, 2020 | 2 | 2020 |
| Sensor placement for contamination detection in water distribution systems M Rebolledo, S Chandrasekaran, T Bartz-Beielstein arXiv preprint arXiv:2011.06406, 2020 | 2 | 2020 |
| A Robust Statistical Framework for the Analysis of the Performances of Stochastic Optimization Algorithms Using the Principles of Severity S Chandrasekaran, T Bartz-Beielstein International Conference on the Applications of Evolutionary Computation …, 2023 | 1 | 2023 |
| Case Study I: Tuning Random Forest (Ranger) T Bartz-Beielstein, S Chandrasekaran, F Rehbach, M Zaefferer Hyperparameter Tuning for Machine and Deep Learning with R: A Practical …, 2023 | 1 | 2023 |
| Case Study IV: Tuned Reinforcement Learning (in Python) M Zaefferer, S Chandrasekaran Hyperparameter Tuning for Machine and Deep Learning with R: A Practical …, 2023 | 1 | 2023 |
| A Novel Ranking Scheme for the Performance Analysis of Stochastic Optimization Algorithms using the Principles of Severity S Chandrasekaran, T Bartz-Beielstein arXiv preprint arXiv:2406.00154, 2024 | | 2024 |
| Benchmarking: Best Practices and Open Issues T Bartz-Beielstein, C Doerr, J Bossek, S Chandrasekaran, T Eftimov, ... | | 2020 |
