| mlr3tuning: Tuning for ‘mlr3’ M Becker, M Lang, J Richter, B Bischl, D Schalk R package version 0.7. 0. URl: https://CRAN. R-project. org/package= mlr3tuning, 2020 | 6 | 2020 |
| Accelerated componentwise gradient boosting using efficient data representation and momentum-based optimization D Schalk, B Bischl, D Rügamer Journal of Computational and Graphical Statistics 32 (2), 631-641, 2023 | 4 | 2023 |
| Automatic Componentwise Boosting: An Interpretable AutoML System S Coors, D Schalk, B Bischl, D Rügamer arXiv preprint arXiv:2109.05583, 2021 | 3 | 2021 |
| compboost: Modular Framework for Component-Wise Boosting D Schalk, J Thomas, B Bischl Journal of Open Source Software 3 (30), 967, 2018 | 3 | 2018 |
| Distributed non-disclosive validation of predictive models by a modified ROC-GLM D Schalk, VS Hoffmann, B Bischl, U Mansmann arXiv preprint arXiv:2203.10828, 2022 | 2 | 2022 |
| Component-wise boosting of targets for multi-output prediction Q Au, D Schalk, G Casalicchio, R Schoedel, C Stachl, B Bischl arXiv preprint arXiv:1904.03943, 2019 | 2 | 2019 |
| Modern approaches for component-wise boosting: Automation, efficiency, and distributed computing with application to the medical domain D Schalk lmu, 2023 | | 2023 |
| dsBinVal: Conducting distributed ROC analysis using DataSHIELD D Schalk, VS Hoffmann, B Bischl, U Mansmann Journal of Open Source Software 8 (82), 4545, 2023 | | 2023 |
| Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models D Schalk, B Bischl, D Rügamer arXiv preprint arXiv:2210.07723, 2022 | | 2022 |
| Efficient and Distributed Model-Based Boosting for Large Datasets D Schalk | | 2018 |
| Supplementary Material for Accelerated Component-wise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization D Schalk, B Bischl, D Rügamer | | |
