| mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions B Bischl, J Richter, J Bossek, D Horn, J Thomas, M Lang arXiv preprint arXiv:1703.03373, 2017 | 217* | 2017 |
| Model-based multi-objective optimization: taxonomy, multi-point proposal, toolbox and benchmark D Horn, T Wagner, D Biermann, C Weihs, B Bischl International Conference on Evolutionary Multi-Criterion Optimization, 64-78, 2015 | 89 | 2015 |
| BBmisc: Miscellaneous helper functions for B. Bischl B Bischl, M Lang, J Bossek, D Horn, J Richter, D Surmann R package version 1.11, 2017 | 55* | 2017 |
| Multi-objective parameter configuration of machine learning algorithms using model-based optimization D Horn, B Bischl Computational Intelligence (SSCI), 2016 IEEE Symposium Series on, 1-8, 2016 | 52 | 2016 |
| A comparative study on large scale kernelized support vector machines D Horn, A Demircioğlu, B Bischl, T Glasmachers, C Weihs Advances in Data Analysis and Classification 12 (4), 867-883, 2018 | 30 | 2018 |
| First Investigations on Noisy Model-Based Multi-objective Optimization D Horn, M Dagge, X Sun, B Bischl International Conference on Evolutionary Multi-Criterion Optimization, 298-313, 2017 | 27 | 2017 |
| A first analysis of kernels for kriging-based optimization in hierarchical search spaces M Zaefferer, D Horn Parallel Problem Solving from Nature–PPSN XV: 15th International Conference …, 2018 | 16 | 2018 |
| Industrial data science: developing a qualification concept for machine learning in industrial production N Bauer, L Stankiewicz, M Jastrow, D Horn, J Teubner, K Kersting, ... European Conference on Data Analysis (ECDA), 2018 | 14 | 2018 |
| Surrogates for hierarchical search spaces: the wedge-kernel and an automated analysis D Horn, J Stork, NJ Schüßler, M Zaefferer Proceedings of the Genetic and Evolutionary Computation Conference, 916-924, 2019 | 12 | 2019 |
| Efficient global optimization: Motivation, variations and applications C Weihs, S Herbrandt, N Bauer, K Friedrichs, D Horn | 10 | 2016 |
| ParamHelpers: Helpers for parameters in black-box optimization, tuning and machine learning B Bischl, M Lang, J Bossek, D Horn, K Schork, J Richter, P Kerschke R package version 1, 23, 2017 | 9 | 2017 |
| Using sequential statistical tests for efficient hyperparameter tuning P Buczak, A Groll, M Pauly, J Rehof, D Horn AStA Advances in Statistical Analysis, 1-20, 2024 | 4 | 2024 |
| Multi-objective selection of algorithm portfolios: Experimental validation D Horn, K Schork, T Wagner Parallel Problem Solving from Nature–PPSN XIV: 14th International Conference …, 2016 | 3 | 2016 |
| Multi-objective Analysis of Machine Learning Algorithms Using Model-based Optimization Techniques D HORN | 2 | 2019 |
| Multi-objective selection of algorithm portfolios D Horn, B Bischl, A Demircioglu, T Glasmachers, T Wagner, C Weihs Archives of Data Science, Series A (Online First) 2 (1), 15s, 2017 | 2 | 2017 |
| Fast model selection by limiting SVM training times A Demircioglu, D Horn, T Glasmachers, B Bischl, C Weihs arXiv preprint arXiv:1602.03368, 2016 | 2 | 2016 |
| Big Data Classification-Many Features, Many Observations C Weihs, D Horn, B Bischl | 2* | |
| Old but Gold or New and Shiny? Comparing Tree Ensembles for Ordinal Prediction with a Classic Parametric Approach P Buczak, D Horn, M Pauly OSF Preprints, 2024 | 1 | 2024 |
| RODD: Robust Outlier Detection in Data Cubes L Kuhlmann, D Wilmes, E Müller, M Pauly, D Horn International Conference on Big Data Analytics and Knowledge Discovery, 325-339, 2023 | 1 | 2023 |
| Contextual Shift Method (CSM) G Schmitz, D Wilmes, A Gerharz, D Horn, E Müller International Conference on Big Data Analytics and Knowledge Discovery, 101-106, 2023 | | 2023 |
Bernd BischlChair of Statistical Learning and Data Science, LMU Munich在 stat.uni-muenchen.de 的电子邮件经过验证
