Jan N. van Rijn

92 papers A* 4B 11Journal 49Unranked 24
YearRankTypeTitle / Venue / Authors
2026 B ed.
Advances in Intelligent Data Analysis XXIV - 24th International Symposium on Intelligent Data Analysis, IDA 2026, Leiden, The Netherlands, April 22-24, 2026, Proceedings
IDA
Mitra Baratchi, Siegfried Nijssen, Jan N. van Rijn
2025 B conf
Beyond the Single-Best Model: Rashomon Partial Dependence Profile for Trustworthy Explanations in AutoML.
DS
Mustafa Cavus, Jan N. van Rijn, Przemyslaw Biecek
2025 J jnl
Beyond the Single-Best Model: Rashomon Partial Dependence Profile for Trustworthy Explanations in AutoML.
CoRR
Mustafa Cavus, Jan N. van Rijn, Przemyslaw Biecek
2025 — conf
Empirical Analysis of Upper Bounds for Robustness Distributions Using Adversarial Attacks.
LION (1)
Aaron Berger, Nils Eberhardt, Annelot W. Bosman, Henning Duwe, Holger H. Hoos, Jan N. van Rijn
2025 B conf
Hubris Benchmarking with AmbiGANs: Assessing Model Overconfidence with Synthetic Ambiguous Data.
DS
Cátia Teixeira, Inês Gomes, Carlos Soares, Jan N. van Rijn
2025 J jnl
OpenML: Insights from 10 years and more than a thousand papers.
Patterns
Bernd Bischl, Giuseppe Casalicchio, Taniya Das, Matthias Feurer, Sebastian Fischer, Pieter Gijsbers, Subhaditya Mukherjee, Andreas C. Müller, László Németh, Luis Oala, Lennart Purucker, Sahithya Ravi, Jan N. van Rijn, Prabhant Singh, Joaquin Vanschoren, Jos van der Velde, Marcel Wever
2025 B conf
Overfitting in Combined Algorithm Selection and Hyperparameter Optimization.
IDA
Sietse Schröder, Mitra Baratchi, Jan N. van Rijn
2025 — ed.
Proceedings of the International Conference on Automated Machine Learning (AutoML 2025), 8-11 September 2025, Cornell Tech, New York, NY, USA
AutoML
Leman Akoglu, Carola Doerr, Jan N. van Rijn, Roman Garnett, Jacob R. Gardner
2025 J jnl
Robustness Distributions in Neural Network Verification.
J. Artif. Intell. Res.
Annelot W. Bosman, Aaron Berger, Holger H. Hoos, Jan N. van Rijn
2025 — conf
Robustness Margin: A New Measure for the Robustness of Neural Networks.
SAIV
Lionel Kielhöfer, Annelot W. Bosman, Holger H. Hoos, Jan N. van Rijn
2024 — conf
A Preliminary Study to Examining Per-class Performance Bias via Robustness Distributions.
SAIV
Annelot W. Bosman, Anna L. Münz, Holger H. Hoos, Jan N. van Rijn
2024 J jnl
AI Competitions and Benchmarks: Dataset Development.
CoRR
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
2024 A* conf
Accelerating Adversarially Robust Model Selection for Deep Neural Networks via Racing.
AAAI
Matthias König, Holger H. Hoos, Jan N. van Rijn
2024 — conf
Automated Design of Linear Bounding Functions for Sigmoidal Nonlinearities in Neural Networks.
ECML/PKDD (7)
Matthias König, Xiyue Zhang, Holger H. Hoos, Marta Kwiatkowska, Jan N. van Rijn
2024 J jnl
Automated Design of Linear Bounding Functions for Sigmoidal Nonlinearities in Neural Networks.
CoRR
Matthias König, Xiyue Zhang, Holger H. Hoos, Marta Kwiatkowska, Jan N. van Rijn
2024 J jnl
Automated machine learning: past, present and future.
Artif. Intell. Rev.
Mitra Baratchi, Can Wang, Steffen Limmer, Jan N. van Rijn, Holger H. Hoos, Thomas Bäck, Markus Olhofer
2024 J jnl
Critically Assessing the State of the Art in Neural Network Verification.
J. Mach. Learn. Res.
Matthias König, Annelot W. Bosman, Holger H. Hoos, Jan N. van Rijn
2024 J jnl
Edge-Based Graph Component Pooling.
CoRR
Thijs Snelleman, Bram M. Renting, Holger H. Hoos, Jan N. van Rijn
2024 J jnl
Finding Patterns in Ambiguity: Interpretable Stress Testing in the Decision Boundary.
CoRR
Inês Gomes, Luís F. Teixeira, Jan N. van Rijn, Carlos Soares, André Restivo, Luís Cunha, Moisés Santos
2024 — conf
GASTeNv2: Generative Adversarial Stress Testing Networks with Gaussian Loss.
EPIA (2)
Cátia Teixeira, Inês Gomes, Luís Cunha, Carlos Soares, Jan N. van Rijn
2024 J jnl
Guest Editorial: AutoML for Nonstationary Data.
IEEE Trans. Artif. Intell.
Ran Cheng, Hugo Jair Escalante, Wei-Wei Tu, Jan N. van Rijn, Shuo Wang, Yun Yang
2024 J jnl
Hyperparameter importance and optimization of quantum neural networks across small datasets.
Mach. Learn.
Charles Moussa, Yash J. Patel, Vedran Dunjko, Thomas Bäck, Jan N. van Rijn
2024 — conf
Learning Curve Extrapolation Methods Across Extrapolation Settings.
IDA (2)
Lionel Kielhöfer, Felix Mohr, Jan N. van Rijn
2024 J jnl
Learning curves for decision making in supervised machine learning: a survey.
Mach. Learn.
Felix Mohr, Jan N. van Rijn
2024 J jnl
Subspace Adaptation Prior for Few-Shot Learning.
Mach. Learn.
Mike Huisman, Aske Plaat, Jan N. van Rijn
2024 J jnl
Understanding transfer learning and gradient-based meta-learning techniques.
Mach. Learn.
Mike Huisman, Aske Plaat, Jan N. van Rijn
2023 J jnl
Are LSTMs Good Few-Shot Learners?
CoRR
Mike Huisman, Thomas M. Moerland, Aske Plaat, Jan N. van Rijn
2023 J jnl
Are LSTMs good few-shot learners?
Mach. Learn.
Mike Huisman, Thomas M. Moerland, Aske Plaat, Jan N. van Rijn
2023 J jnl
Artificial intelligence to advance Earth observation: a perspective.
CoRR
Devis Tuia, Konrad Schindler, Begüm Demir, Gustau Camps-Valls, Xiao Xiang Zhu, Mrinalini Kochupillai, Saso Dzeroski, Jan N. van Rijn, Holger H. Hoos, Fabio Del Frate, Mihai Datcu, Jorge-Arnulfo Quiané-Ruiz, Volker Markl, Bertrand Le Saux, Rochelle Schneider
2023 — conf
Critically Assessing the State of the Art in CPU-based Local Robustness Verification.
SafeAI@AAAI
Matthias König, Annelot W. Bosman, Holger H. Hoos, Jan N. van Rijn
2023 J jnl
Fast and Informative Model Selection Using Learning Curve Cross-Validation.
IEEE Trans. Pattern Anal. Mach. Intell.
Felix Mohr, Jan N. van Rijn
2023 J jnl
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification.
CoRR
Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera, Isabelle Guyon, Mike Huisman, Felix Mohr, Jan N. van Rijn, Haozhe Sun, Joaquin Vanschoren, Phan Anh Vu
2023 J jnl
Subspace Adaptation Prior for Few-Shot Learning.
CoRR
Mike Huisman, Aske Plaat, Jan N. van Rijn
2023 J jnl
Understanding Transfer Learning and Gradient-Based Meta-Learning Techniques.
CoRR
Mike Huisman, Aske Plaat, Jan N. van Rijn
2022 J jnl
Advances in MetaDL: AAAI 2021 challenge and workshop.
CoRR
Adrian El Baz, Isabelle Guyon, Zhengying Liu, Jan N. van Rijn, Sébastien Treguer, Joaquin Vanschoren
2022 — conf
Advances in Metalearning: ECML/PKDD Workshop on Meta-Knowledge Transfer.
Meta-Knowledge Transfer @ ECML/PKDD
Pavel Brazdil, Jan N. van Rijn, Henry Gouk, Felix Mohr
2022 — ed.
ECML/PKDD Workshop on Meta-Knowledge Transfer, 23 September 2022, Grenoble, France
Meta-Knowledge Transfer @ ECML/PKDD
Pavel Brazdil, Jan N. van Rijn, Henry Gouk, Felix Mohr
2022 B conf
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets.
DS
Charles Moussa, Jan N. van Rijn, Thomas Bäck, Vedran Dunjko
2022 J jnl
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets.
CoRR
Charles Moussa, Jan N. van Rijn, Thomas Bäck, Vedran Dunjko
2022 — conf
LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks.
ECML/PKDD (5)
Felix Mohr, Tom J. Viering, Marco Loog, Jan N. van Rijn
2022 J jnl
Learning Curves for Decision Making in Supervised Machine Learning - A Survey.
CoRR
Felix Mohr, Jan N. van Rijn
2022 J jnl
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification.
CoRR
Adrian El Baz, André C. P. L. F. de Carvalho, Hong Chen, Fabio Ferreira, Henry Gouk, Shell Hu, Frank Hutter, Zhengying Liu, Felix Mohr, Jan N. van Rijn, Xin Wang, Isabelle Guyon
2022 A* conf
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification.
NeurIPS
Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera, Isabelle Guyon, Mike Huisman, Felix Mohr, Jan N. van Rijn, Haozhe Sun, Joaquin Vanschoren, Phan Anh Vu
2022 J jnl
Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio.
Mach. Learn.
Matthias König, Holger H. Hoos, Jan N. van Rijn
2022 J jnl
Stateless neural meta-learning using second-order gradients.
Mach. Learn.
Mike Huisman, Aske Plaat, Jan N. van Rijn
2021 J jnl
A survey of deep meta-learning.
Artif. Intell. Rev.
Mike Huisman, Jan N. van Rijn, Aske Plaat
2021 — ed.
AAAI Workshop on Meta-Learning and MetaDL Challenge, MetaDL@AAAI 2021, virtual, February 9, 2021.
MetaDL@AAAI
Isabelle Guyon, Jan N. van Rijn, Sébastien Treguer, Joaquin Vanschoren
2021 — conf
Advances in MetaDL: AAAI 2021 Challenge and Workshop.
MetaDL@AAAI
Adrian El Baz, Isabelle Guyon, Zhengying Liu, Jan N. van Rijn, Sébastien Treguer, Joaquin Vanschoren
2021 — ed.
Artificial Intelligence and Machine Learning - 32nd Benelux Conference, BNAIC/Benelearn 2020, Leiden, The Netherlands, November 19-20, 2020, Revised Selected Papers
BNAIC/BENELEARN (Selected Papers)
Mitra Baratchi, Lu Cao, Walter A. Kosters, Jefrey Lijffijt, Jan N. van Rijn, Frank W. Takes
2021 — conf
Automated Machine Learning for Satellite Data: Integrating Remote Sensing Pre-trained Models into AutoML Systems.
ECML/PKDD (5)
Nelly Rosaura Palacios Salinas, Mitra Baratchi, Jan N. van Rijn, Andreas Vollrath
2021 B conf
Automatic Human-Like Detection of Code Smells.
DS
Chitsutha Soomlek, Jan N. van Rijn, Marcello M. Bonsangue
2021 J jnl
Fast and Informative Model Selection using Learning Curve Cross-Validation.
CoRR
Felix Mohr, Jan N. van Rijn
2021 — conf
Learning multiple defaults for machine learning algorithms.
GECCO Companion
Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas C. Müller, Bernd Bischl
2021 — conf
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification.
NeurIPS (Competition and Demos)
Adrian El Baz, Ihsan Ullah, Edesio Alcobaça, André C. P. L. F. de Carvalho, Hong Chen, Fabio Ferreira, Henry Gouk, Chaoyu Guan, Isabelle Guyon, Timothy M. Hospedales, Shell Hu, Mike Huisman, Frank Hutter, Zhengying Liu, Felix Mohr, Ekrem Öztürk, Jan N. van Rijn, Haozhe Sun, Xin Wang, Wenwu Zhu
2021 J jnl
Meta-Learning for Symbolic Hyperparameter Defaults.
CoRR
Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren
2021 — conf
Meta-learning for symbolic hyperparameter defaults.
GECCO Companion
Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren
2021 — conf
OpenML Benchmarking Suites.
NeurIPS Datasets and Benchmarks
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren
2021 J jnl
OpenML-Python: an extensible Python API for OpenML.
J. Mach. Learn. Res.
Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter
2021 J jnl
Stateless Neural Meta-Learning using Second-Order Gradients.
CoRR
Mike Huisman, Aske Plaat, Jan N. van Rijn
2020 J jnl
A Survey of Deep Meta-Learning.
CoRR
Mike Huisman, Jan N. van Rijn, Aske Plaat
2020 — conf
Eating Sound Dataset for 20 Food Types and Sound Classification Using Convolutional Neural Networks.
ICMI Companion
Jeannette Shijie Ma, Marcello A. Gómez Maureira, Jan N. van Rijn
2019 B conf
Hyperparameter Importance for Image Classification by Residual Neural Networks.
DS
Abhinav Sharma, Jan N. van Rijn, Frank Hutter, Andreas Müller
2019 J jnl
Multi-task learning with a natural metric for quantitative structure activity relationship learning.
J. Cheminformatics
Noureddin Sadawi, Iván Olier, Joaquin Vanschoren, Jan N. van Rijn, Jeremy Besnard, G. Richard J. Bickerton, Crina Grosan, Larisa N. Soldatova, Ross D. King
2019 J jnl
OpenML-Python: an extensible Python API for OpenML.
CoRR
Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter
2019 J jnl
The algorithm selection competitions 2015 and 2017.
Artif. Intell.
Marius Lindauer, Jan N. van Rijn, Lars Kotthoff
2018 — conf
Computing and Predicting Winning Hands in the Trick-Taking Game of Klaverjas.
BNCAI
Jan N. van Rijn, Frank W. Takes, Jonathan K. Vis
2018 B conf
Don't Rule Out Simple Models Prematurely: A Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML.
IDA
Benjamin Strang, Peter van der Putten, Jan N. van Rijn, Frank Hutter
2018 A* conf
Hyperparameter Importance Across Datasets.
KDD
Jan N. van Rijn, Frank Hutter
2018 J jnl
Learning Multiple Defaults for Machine Learning Algorithms.
CoRR
Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas C. Müller, Bernd Bischl
2018 J jnl
Speeding up algorithm selection using average ranking and active testing by introducing runtime.
Mach. Learn.
Salisu Mamman Abdulrahman, Pavel Brazdil, Jan N. van Rijn, Joaquin Vanschoren
2018 J jnl
The Algorithm Selection Competition Series 2015-17.
CoRR
Marius Lindauer, Jan N. van Rijn, Lars Kotthoff
2018 J jnl
The online performance estimation framework: heterogeneous ensemble learning for data streams.
Mach. Learn.
Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren
2017 — conf
An Empirical Study of Hyperparameter Importance Across Datasets.
AutoML@PKDD/ECML
Jan N. van Rijn, Frank Hutter
2017 J jnl
Hyperparameter Importance Across Datasets.
CoRR
Jan N. van Rijn, Frank Hutter
2017 — conf
Open Algorithm Selection Challenge 2017: Setup and Scenarios.
OASC
Marius Lindauer, Jan N. van Rijn, Lars Kotthoff
2017 J jnl
OpenML Benchmarking Suites and the OpenML100.
CoRR
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren
2016 J jnl
Acyclic Constraint Logic and Games.
CoRR
Hendrik Jan Hoogeboom, Walter A. Kosters, Jan N. van Rijn, Jonathan K. Vis
2016 B conf
Does Feature Selection Improve Classification? A Large Scale Experiment in OpenML.
IDA
Martijn J. Post, Peter van der Putten, Jan N. van Rijn
2016 J jnl
Endgame Analysis of Dou Shou Qi.
CoRR
Jan N. van Rijn, Jonathan K. Vis
2016 J jnl
The Complexity of Rummikub Problems.
CoRR
Jan N. van Rijn, Frank W. Takes, Jonathan K. Vis
2015 — conf
Algorithm Selection via Meta-learning and Sample-based Active Testing.
MetaSel@PKDD/ECML
Salisu Mamman Abdulrahman, Pavel Brazdil, Jan N. van Rijn, Joaquin Vanschoren
2015 B conf
Fast Algorithm Selection Using Learning Curves.
IDA
Jan N. van Rijn, Salisu Mamman Abdulrahman, Pavel Brazdil, Joaquin Vanschoren
2015 A* conf
Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams.
ICDM
Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren
2015 — conf
Sharing RapidMiner Workflows and Experiments with OpenML.
MetaSel@PKDD/ECML
Jan N. van Rijn, Joaquin Vanschoren
2015 — conf
Taking machine learning research online with OpenML.
BigMine
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl
2014 J jnl
Acyclic Constraint Logic and Games.
ICGA J.
Hendrik Jan Hoogeboom, Walter A. Kosters, Jan N. van Rijn, Jonathan K. Vis
2014 B conf
Algorithm Selection on Data Streams.
Discovery Science
Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren
2014 J jnl
Endgame Analysis of DOU SHOU QI.
ICGA J.
Jan N. van Rijn, Jonathan K. Vis
2014 J jnl
OpenML: networked science in machine learning.
CoRR
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, Luís Torgo
2014 — conf
Towards Meta-learning over Data Streams.
MetaSel@ECAI
Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren
2013 — conf
OpenML: A Collaborative Science Platform.
ECML/PKDD (3)
Jan N. van Rijn, Bernd Bischl, Luís Torgo, Bo Gao, Venkatesh Umaashankar, Simon Fischer, Patrick Winter, Bernd Wiswedel, Michael R. Berthold, Joaquin Vanschoren
2013 J jnl
OpenML: networked science in machine learning.
SIGKDD Explor.
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, Luís Torgo