OpenML: A Collaborative Science Platform. - Jan N. van Rijn; Bernd Bischl; Luís Torgo; Bo Gao 002; Venkatesh Umaashankar; Simon Fischer 001; Patrick Winter; Bernd Wiswedel; Michael R. Berthold; Joaquin Vanschoren (ECML/PKDD (3), 2013)

OpenML: networked science in machine learning. - Joaquin Vanschoren; Jan N. van Rijn; Bernd Bischl; Luís Torgo (SIGKDD Explor., 2013)

Algorithm Selection on Data Streams. - Jan N. van Rijn; Geoffrey Holmes 001; Bernhard Pfahringer; Joaquin Vanschoren (Discovery Science, 2014)

Towards Meta-learning over Data Streams. - Jan N. van Rijn; Geoffrey Holmes 001; Bernhard Pfahringer; Joaquin Vanschoren (MetaSel@ECAI, 2014)

Acyclic Constraint Logic and Games. - Hendrik Jan Hoogeboom; Walter A. Kosters; Jan N. van Rijn; Jonathan K. Vis (J. Int. Comput. Games Assoc., 2014)

Endgame Analysis of DOU SHOU QI. - Jan N. van Rijn; Jonathan K. Vis (J. Int. Comput. Games Assoc., 2014)

Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams. - Jan N. van Rijn; Geoffrey Holmes 001; Bernhard Pfahringer; Joaquin Vanschoren (ICDM, 2015)

Fast Algorithm Selection Using Learning Curves. - Jan N. van Rijn; Salisu Mamman Abdulrahman; Pavel Brazdil; Joaquin Vanschoren (IDA, 2015)

Taking machine learning research online with OpenML. - Joaquin Vanschoren; Jan N. van Rijn; Bernd Bischl (BigMine, 2015)

Algorithm Selection via Meta-learning and Sample-based Active Testing. - Salisu Mamman Abdulrahman; Pavel Brazdil; Jan N. van Rijn; Joaquin Vanschoren (MetaSel@PKDD/ECML, 2015)

Sharing RapidMiner Workflows and Experiments with OpenML. - Jan N. van Rijn; Joaquin Vanschoren (MetaSel@PKDD/ECML, 2015)

Does Feature Selection Improve Classification? A Large Scale Experiment in OpenML. - Martijn J. Post; Peter van der Putten; Jan N. van Rijn (IDA, 2016)

The Complexity of Rummikub Problems. - Jan N. van Rijn; Frank W. Takes; Jonathan K. Vis (CoRR, 2016)

Open Algorithm Selection Challenge 2017: Setup and Scenarios. - Marius Lindauer; Jan N. van Rijn; Lars Kotthoff (OASC, 2017)

An Empirical Study of Hyperparameter Importance Across Datasets. - Jan N. van Rijn; Frank Hutter (AutoML@PKDD/ECML, 2017)

OpenML Benchmarking Suites and the OpenML100. - Bernd Bischl; Giuseppe Casalicchio; Matthias Feurer; Frank Hutter; Michel Lang; Rafael Gomes Mantovani; Jan N. van Rijn; Joaquin Vanschoren (CoRR, 2017)

Computing and Predicting Winning Hands in the Trick-Taking Game of Klaverjas. - Jan N. van Rijn; Frank W. Takes; Jonathan K. Vis (BNCAI, 2018)

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 (IDA, 2018)

Hyperparameter Importance Across Datasets. - Jan N. van Rijn; Frank Hutter (KDD, 2018)

The Algorithm Selection Competition Series 2015-17. - Marius Lindauer; Jan N. van Rijn; Lars Kotthoff (CoRR, 2018)

Speeding up algorithm selection using average ranking and active testing by introducing runtime. - Salisu Mamman Abdulrahman; Pavel Brazdil; Jan N. van Rijn; Joaquin Vanschoren (Mach. Learn., 2018)

The online performance estimation framework: heterogeneous ensemble learning for data streams. - Jan N. van Rijn; Geoffrey Holmes 001; Bernhard Pfahringer; Joaquin Vanschoren (Mach. Learn., 2018)

Hyperparameter Importance for Image Classification by Residual Neural Networks. - Abhinav Sharma; Jan N. van Rijn; Frank Hutter; Andreas Müller 004 (DS, 2019)

The algorithm selection competitions 2015 and 2017. - Marius Lindauer; Jan N. van Rijn; Lars Kotthoff (Artif. Intell., 2019)

Multi-task learning with a natural metric for quantitative structure activity relationship learning. - Noureddin Sadawi; Iván Olier; Joaquin Vanschoren; Jan N. van Rijn; Jeremy Besnard; G. Richard J. Bickerton; Crina Grosan; Larisa N. Soldatova; Ross D. King (J. Cheminformatics, 2019)

Eating Sound Dataset for 20 Food Types and Sound Classification Using Convolutional Neural Networks. - Jeannette Shijie Ma; Marcello A. Gómez Maureira; Jan N. van Rijn (ICMI Companion, 2020)

Advances in MetaDL: AAAI 2021 Challenge and Workshop. - Adrian El Baz; Isabelle Guyon; Zhengying Liu; Jan N. van Rijn; Sébastien Treguer; Joaquin Vanschoren (MetaDL@AAAI, 2021)

Automatic Human-Like Detection of Code Smells. - Chitsutha Soomlek; Jan N. van Rijn; Marcello M. Bonsangue (DS, 2021)

Meta-learning for symbolic hyperparameter defaults. - Pieter Gijsbers; Florian Pfisterer; Jan N. van Rijn; Bernd Bischl; Joaquin Vanschoren (GECCO Companion, 2021)

Learning multiple defaults for machine learning algorithms. - Florian Pfisterer; Jan N. van Rijn; Philipp Probst; Andreas C. Müller 002; Bernd Bischl (GECCO Companion, 2021)

Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification. - Adrian El Baz; Ihsan Ullah; Edesio Alcobaça; André C. P. L. F. de Carvalho; Hong Chen 011; Fabio Ferreira; Henry Gouk; Chaoyu Guan; Isabelle Guyon; Timothy M. Hospedales; Shell Hu; Mike Huisman; et al. (NeurIPS (Competition and Demos), 2021)

OpenML Benchmarking Suites. - Bernd Bischl; Giuseppe Casalicchio; Matthias Feurer; Pieter Gijsbers; Frank Hutter; Michel Lang; Rafael Gomes Mantovani; Jan N. van Rijn; Joaquin Vanschoren (NeurIPS Datasets and Benchmarks, 2021)

Automated Machine Learning for Satellite Data: Integrating Remote Sensing Pre-trained Models into AutoML Systems. - Nelly Rosaura Palacios Salinas; Mitra Baratchi; Jan N. van Rijn; Andreas Vollrath (ECML/PKDD (5), 2021)

A survey of deep meta-learning. - Mike Huisman; Jan N. van Rijn; Aske Plaat (Artif. Intell. Rev., 2021)

OpenML-Python: an extensible Python API for OpenML. - Matthias Feurer; Jan N. van Rijn; Arlind Kadra; Pieter Gijsbers; Neeratyoy Mallik; Sahithya Ravi; Andreas Müller 004; Joaquin Vanschoren; Frank Hutter (J. Mach. Learn. Res., 2021)

Hyperparameter Importance of Quantum Neural Networks Across Small Datasets. - Charles Moussa; Jan N. van Rijn; Thomas Bäck; Vedran Dunjko (DS, 2022)

Advances in Metalearning: ECML/PKDD Workshop on Meta-Knowledge Transfer. - Pavel Brazdil; Jan N. van Rijn; Henry Gouk; Felix Mohr (Meta-Knowledge Transfer @ ECML/PKDD, 2022)

Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification. - Ihsan Ullah; Dustin Carrión-Ojeda; Sergio Escalera; Isabelle Guyon; Mike Huisman; Felix Mohr; Jan N. van Rijn; Haozhe Sun; Joaquin Vanschoren; Phan Anh Vu (NeurIPS, 2022)

LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks. - Felix Mohr; Tom J. Viering; Marco Loog; Jan N. van Rijn (ECML/PKDD (5), 2022)

Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification. - Adrian El Baz; André C. P. L. F. de Carvalho; Hong Chen 011; Fabio Ferreira; Henry Gouk; Shell Hu; Frank Hutter; Zhengying Liu; Felix Mohr; Jan N. van Rijn; Xin Wang 019; Isabelle Guyon (CoRR, 2022)

Stateless neural meta-learning using second-order gradients. - Mike Huisman; Aske Plaat; Jan N. van Rijn (Mach. Learn., 2022)

Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio. - Matthias König 005; Holger H. Hoos; Jan N. van Rijn (Mach. Learn., 2022)

Critically Assessing the State of the Art in CPU-based Local Robustness Verification. - Matthias König 005; Annelot W. Bosman; Holger H. Hoos; Jan N. van Rijn (SafeAI@AAAI, 2023)

Artificial intelligence to advance Earth observation: a perspective. - Devis Tuia; Konrad Schindler; Begüm Demir; Gustau Camps-Valls; Xiao Xiang Zhu 001; Mrinalini Kochupillai; Saso Dzeroski; Jan N. van Rijn; Holger H. Hoos; Fabio Del Frate; Mihai Datcu; Jorge-Arnulfo Quiané-Ruiz; et al. (CoRR, 2023)

Fast and Informative Model Selection Using Learning Curve Cross-Validation. - Felix Mohr; Jan N. van Rijn (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Are LSTMs good few-shot learners? - Mike Huisman; Thomas M. Moerland; Aske Plaat; Jan N. van Rijn (Mach. Learn., 2023)

Accelerating Adversarially Robust Model Selection for Deep Neural Networks via Racing. - Matthias König 005; Holger H. Hoos; Jan N. van Rijn (AAAI, 2024)

GASTeNv2: Generative Adversarial Stress Testing Networks with Gaussian Loss. - Cátia Teixeira; Inês Gomes 001; Luís Cunha; Carlos Soares; Jan N. van Rijn (EPIA (2), 2024)

Learning Curve Extrapolation Methods Across Extrapolation Settings. - Lionel Kielhöfer; Felix Mohr; Jan N. van Rijn (IDA (2), 2024)

Automated Design of Linear Bounding Functions for Sigmoidal Nonlinearities in Neural Networks. - Matthias König 005; Xiyue Zhang 001; Holger H. Hoos; Marta Kwiatkowska; Jan N. van Rijn (ECML/PKDD (7), 2024)

A Preliminary Study to Examining Per-class Performance Bias via Robustness Distributions. - Annelot W. Bosman; Anna L. Münz; Holger H. Hoos; Jan N. van Rijn (SAIV, 2024)

AI Competitions and Benchmarks: Dataset Development. - Romain Egele; Júlio C. S. Jacques Júnior; Jan N. van Rijn; Isabelle Guyon; Xavier Baró; Albert Clapés; Prasanna Balaprakash; Sergio Escalera; Thomas B. Moeslund; Jun Wan 001 (CoRR, 2024)

Finding Patterns in Ambiguity: Interpretable Stress Testing in the Decision Boundary. - Inês Gomes 001; Luís F. Teixeira 001; Jan N. van Rijn; Carlos Soares; André Restivo; Luís Cunha; Moisés Santos (CoRR, 2024)

Edge-Based Graph Component Pooling. - Thijs Snelleman; Bram M. Renting; Holger H. Hoos; Jan N. van Rijn (CoRR, 2024)

Critically Assessing the State of the Art in Neural Network Verification. - Matthias König 005; Annelot W. Bosman; Holger H. Hoos; Jan N. van Rijn (J. Mach. Learn. Res., 2024)

Subspace Adaptation Prior for Few-Shot Learning. - Mike Huisman; Aske Plaat; Jan N. van Rijn (Mach. Learn., 2024)

Hyperparameter importance and optimization of quantum neural networks across small datasets. - Charles Moussa; Yash J. Patel; Vedran Dunjko; Thomas Bäck; Jan N. van Rijn (Mach. Learn., 2024)

Automated machine learning: past, present and future. - Mitra Baratchi; Can Wang; Steffen Limmer; Jan N. van Rijn; Holger H. Hoos; Thomas Bäck; Markus Olhofer (Artif. Intell. Rev., 2024)

Guest Editorial: AutoML for Nonstationary Data. - Ran Cheng; Hugo Jair Escalante; Wei-Wei Tu; Jan N. van Rijn; Shuo Wang 005; Yun Yang 003 (IEEE Trans. Artif. Intell., 2024)

Understanding transfer learning and gradient-based meta-learning techniques. - Mike Huisman; Aske Plaat; Jan N. van Rijn (Mach. Learn., 2024)

Learning curves for decision making in supervised machine learning: a survey. - Felix Mohr; Jan N. van Rijn (Mach. Learn., 2024)

Overfitting in Combined Algorithm Selection and Hyperparameter Optimization. - Sietse Schröder; Mitra Baratchi; Jan N. van Rijn (IDA, 2025)

Robustness Distributions in Neural Network Verification. - Annelot W. Bosman; Aaron Berger; Holger H. Hoos; Jan N. van Rijn (J. Artif. Intell. Res., 2025)

OpenML: Insights from 10 years and more than a thousand papers. - Bernd Bischl; Giuseppe Casalicchio; Taniya Das; Matthias Feurer; Sebastian Fischer; Pieter Gijsbers; Subhaditya Mukherjee; Andreas C. Müller 002; László Németh; Luis Oala; Lennart Purucker; Sahithya Ravi; et al. (Patterns, 2025)

Beyond the Single-Best Model: Rashomon Partial Dependence Profile for Trustworthy Explanations in AutoML. - Mustafa Cavus; Jan N. van Rijn; Przemyslaw Biecek (CoRR, 2025)