| 2025 | DIS | Beyond the Single-Best Model: Rashomon Partial Dependence Profile for Trustworthy Explanations in AutoML. | Mustafa Cavus, Jan N. van Rijn, Przemyslaw Biecek |
| 2025 | DIS | Hubris Benchmarking with AmbiGANs: Assessing Model Overconfidence with Synthetic Ambiguous Data. | Ctia Teixeira, Ins Gomes, Carlos Soares, Jan N. van Rijn |
| 2025 | IDA | Overfitting in Combined Algorithm Selection and Hyperparameter Optimization. | Sietse Schrder, Mitra Baratchi, Jan N. van Rijn |
| 2024 | AAAI | Accelerating Adversarially Robust Model Selection for Deep Neural Networks via Racing. | Matthias Knig, Holger H. Hoos, Jan N. van Rijn |
| 2024 | EPIA | GASTeNv2: Generative Adversarial Stress Testing Networks with Gaussian Loss. | Ctia Teixeira, Ins Gomes, Lus Cunha, Carlos Soares, Jan N. van Rijn |
| 2024 | IDA | Learning Curve Extrapolation Methods Across Extrapolation Settings. | Lionel Kielhfer, Felix Mohr, Jan N. van Rijn |
| 2023 | AAAI | Critically Assessing the State of the Art in CPU-based Local Robustness Verification. | Matthias Knig, Annelot W. Bosman, Holger H. Hoos, Jan N. van Rijn |
| 2022 | DIS | Hyperparameter Importance of Quantum Neural Networks Across Small Datasets. | Charles Moussa, Jan N. van Rijn, Thomas Bck, Vedran Dunjko |
| 2021 | AAAI | Advances in MetaDL: AAAI 2021 Challenge and Workshop. | Adrian El Baz, Isabelle Guyon, Zhengying Liu, Jan N. van Rijn, Sbastien Treguer, Joaquin Vanschoren |
| 2021 | DIS | Automatic Human-Like Detection of Code Smells. | Chitsutha Soomlek, Jan N. van Rijn, Marcello M. Bonsangue |
| 2021 | GECCO | Meta-learning for symbolic hyperparameter defaults. | Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren |
| 2021 | GECCO | Learning multiple defaults for machine learning algorithms. | Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas C. Mller, Bernd Bischl |
| 2020 | ICMI | Eating Sound Dataset for 20 Food Types and Sound Classification Using Convolutional Neural Networks. | Jeannette Shijie Ma, Marcello A. Gmez Maureira, Jan N. van Rijn |
| 2019 | DIS | Hyperparameter Importance for Image Classification by Residual Neural Networks. | Abhinav Sharma, Jan N. van Rijn, Frank Hutter, Andreas Mller |
| 2018 | IDA | Don't Rule Out Simple Models Prematurely: A Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML. | Benjamin Strang, Peter van der Putten, Jan N. van Rijn, Frank Hutter |
| 2018 | KDD | Hyperparameter Importance Across Datasets. | Jan N. van Rijn, Frank Hutter |
| 2016 | IDA | Does Feature Selection Improve Classification? A Large Scale Experiment in OpenML. | Martijn J. Post, Peter van der Putten, Jan N. van Rijn |
| 2015 | ICDM | Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams. | Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren |
| 2015 | IDA | Fast Algorithm Selection Using Learning Curves. | Jan N. van Rijn, Salisu Mamman Abdulrahman, Pavel Brazdil, Joaquin Vanschoren |
| 2015 | KDD | Taking machine learning research online with OpenML. | Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl |
| 2014 | DIS | Algorithm Selection on Data Streams. | Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren |
| 2014 | ECAI | Towards Meta-learning over Data Streams. | Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren |