| Multi-Objective Hyperparameter Optimization in Machine Learning – An Overview F Karl, T Pielok, J Moosbauer, F Pfisterer, S Coors, M Binder, L Schneider, ... ACM Transactions on Evolutionary Learning, 2023 | 72 | 2023 |
| mirt: Multidimensional item response theory P Chalmers, J Pritikin, A Robitzsch, M Zoltak, K Kim, CF Falk, A Meade, ... R package version 1 (1), 2020 | 70* | 2020 |
| nonnest2: Tests of non-nested models E Merkle, D You, L Schneider, S Bae R Package Version 0.5-5, 2020 | 48 | 2020 |
| YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization F Pfisterer*, L Schneider*, J Moosbauer, M Binder, B Bischl First Conference on Automated Machine Learning (Main Track), 2022 | 47 | 2022 |
| An R toolbox for score-based measurement invariance tests in IRT models L Schneider, C Strobl, A Zeileis, R Debelak Behavior Research Methods, 1-13, 2021 | 33 | 2021 |
| mlr3pipelines - Flexible Machine Learning Pipelines in R M Binder, F Pfisterer, M Lang, L Schneider, L Kotthoff, B Bischl J. Mach. Learn. Res. 22, 184:1-184:7, 2021 | 32 | 2021 |
| Model Selection of Nested and Non-Nested Item Response Models using Vuong Tests L Schneider, RP Chalmers, R Debelak, EC Merkle Multivariate Behavioral Research, 1-21, 2019 | 32 | 2019 |
| psychotools: Infrastructure for psychometric modeling A Zeileis, C Strobl, F Wickelmaier, B Komboz, J Kopf, L Schneider, ... R package version 0.1-1, URL http://CRAN. R-project. org/package= psychotools, 2011 | 26* | 2011 |
| A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses H Seibold, S Czerny, S Decke, R Dieterle, T Eder, S Fohr, N Hahn, ... Plos one 16 (6), e0251194, 2021 | 25 | 2021 |
| HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis L Schneider*, L Schäpermeier*, RP Prager*, B Bischl, H Trautmann, ... International Conference on Parallel Problem Solving from Nature, 575-589, 2022 | 14 | 2022 |
| Automated benchmark-driven design and explanation of hyperparameter optimizers J Moosbauer, M Binder, L Schneider, F Pfisterer, M Becker, M Lang, ... IEEE Transactions on Evolutionary Computation 26 (6), 1336-1350, 2022 | 12 | 2022 |
| Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models L Schneider, B Bischl, J Thomas Proceedings of the Genetic and Evolutionary Computation Conference, 538-547, 2023 | 8 | 2023 |
| Mutation is all you need L Schneider, F Pfisterer, M Binder, B Bischl 8th ICML Workshop on Automated Machine Learning, 2021 | 8 | 2021 |
| Not all data are created equal: Lessons from sampling theory for adaptive machine learning J Rodemann, S Fischer, L Schneider, M Nalenz, T Augustin | 7 | 2022 |
| mlr3: Machine learning in R - Next generation M Lang, B Bischl, J Richter, P Schratz, G Casalicchio, S Coors, Q Au, ... | 7 | 2020 |
| Tackling Neural Architecture Search With Quality Diversity Optimization L Schneider, F Pfisterer, P Kent, J Branke, B Bischl, J Thomas First Conference on Automated Machine Learning (Main Track), 2022 | 6 | 2022 |
| Using the raschtree function for detecting differential item functioning in the Rasch model C Strobl, L Schneider, J Kopf, A Zeileis | 6 | 2021 |
| A collection of quality diversity optimization problems derived from hyperparameter optimization of machine learning models L Schneider, F Pfisterer, J Thomas, B Bischl Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2022 | 4 | 2022 |
| Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features RP Prager, K Dietrich, L Schneider, L Schäpermeier, B Bischl, P Kerschke, ... Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic …, 2023 | 3 | 2023 |
| Correction: A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses H Seibold, S Czerny, S Decke, R Dieterle, T Eder, S Fohr, N Hahn, ... Plos one 17 (5), e0269047, 2022 | 3 | 2022 |
