Jan N. van Rijn

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 60

Top Topics

Model Selection

Learning Curves

Top Venues

Publications

Mitra Baratchi, Can Wang, Steffen Limmer, Jan N. van Rijn, Holger H. Hoos, Thomas Bäck, Markus Olhofer
Automated machine learning: past, present and future. Artif. Intell. Rev. 57 (5) (2024)
Mike Huisman, Aske Plaat, Jan N. van Rijn
Subspace Adaptation Prior for Few-Shot Learning. Mach. Learn. 113 (2) (2024)
Lionel Kielhöfer, Felix Mohr, Jan N. van Rijn
Learning Curve Extrapolation Methods Across Extrapolation Settings. IDA (2) (2024)
Charles Moussa, Yash J. Patel, Vedran Dunjko, Thomas Bäck, Jan N. van Rijn
Hyperparameter importance and optimization of quantum neural networks across small datasets. Mach. Learn. 113 (4) (2024)
Ran Cheng, Hugo Jair Escalante, Wei-Wei Tu, Jan N. van Rijn, Shuo Wang, Yun Yang
Guest Editorial: AutoML for Nonstationary Data. IEEE Trans. Artif. Intell. 5 (6) (2024)
Mike Huisman, Aske Plaat, Jan N. van Rijn
Understanding transfer learning and gradient-based meta-learning techniques. Mach. Learn. 113 (7) (2024)
Matthias König, Holger H. Hoos, Jan N. van Rijn
Accelerating Adversarially Robust Model Selection for Deep Neural Networks via Racing. AAAI (2024)
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
AI Competitions and Benchmarks: Dataset Development. CoRR (2024)
Matthias König, Xiyue Zhang, Holger H. Hoos, Marta Kwiatkowska, Jan N. van Rijn
Automated Design of Linear Bounding Functions for Sigmoidal Nonlinearities in Neural Networks. CoRR (2024)
Mike Huisman, Aske Plaat, Jan N. van Rijn
Subspace Adaptation Prior for Few-Shot Learning. CoRR (2023)
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
Artificial intelligence to advance Earth observation: a perspective. CoRR (2023)
Mike Huisman, Thomas M. Moerland, Aske Plaat, Jan N. van Rijn
Are LSTMs Good Few-Shot Learners? CoRR (2023)
Mike Huisman, Thomas M. Moerland, Aske Plaat, Jan N. van Rijn
Are LSTMs good few-shot learners? Mach. Learn. 112 (11) (2023)
Matthias König, Annelot Bosman, Holger H. Hoos, Jan N. van Rijn
Critically Assessing the State of the Art in CPU-based Local Robustness Verification. SafeAI@AAAI (2023)
Felix Mohr, Jan N. van Rijn
Fast and Informative Model Selection Using Learning Curve Cross-Validation. IEEE Trans. Pattern Anal. Mach. Intell. 45 (8) (2023)
Mike Huisman, Aske Plaat, Jan N. van Rijn
Understanding Transfer Learning and Gradient-Based Meta-Learning Techniques. CoRR (2023)
Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera, Isabelle Guyon, Mike Huisman, Felix Mohr, Jan N. van Rijn, Haozhe Sun, Joaquin Vanschoren, Phan Anh Vu
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification. CoRR (2023)
Adrian El Baz, Isabelle Guyon, Zhengying Liu, Jan N. van Rijn, Sébastien Treguer, Joaquin Vanschoren
Advances in MetaDL: AAAI 2021 challenge and workshop. CoRR (2022)
Matthias König, Holger H. Hoos, Jan N. van Rijn
Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio. Mach. Learn. 111 (12) (2022)
Felix Mohr, Jan N. van Rijn
Learning Curves for Decision Making in Supervised Machine Learning - A Survey. CoRR (2022)
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
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification. CoRR (2022)
Charles Moussa, Jan N. van Rijn, Thomas Bäck, Vedran Dunjko
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets. CoRR (2022)
Pavel Brazdil, Jan N. van Rijn, Henry Gouk, Felix Mohr
Advances in Metalearning: ECML/PKDD Workshop on Meta-Knowledge Transfer. Meta-Knowledge Transfer @ ECML/PKDD (2022)
Mike Huisman, Aske Plaat, Jan N. van Rijn
Stateless neural meta-learning using second-order gradients. Mach. Learn. 111 (9) (2022)
Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera, Isabelle Guyon, Mike Huisman, Felix Mohr, Jan N. van Rijn, Haozhe Sun, Joaquin Vanschoren, Phan Anh Vu
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification. NeurIPS (2022)
Charles Moussa, Jan N. van Rijn, Thomas Bäck, Vedran Dunjko
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets. DS (2022)
Felix Mohr, Tom J. Viering, Marco Loog, Jan N. van Rijn
LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks. ECML/PKDD (5) (2022)
Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren
Meta-Learning for Symbolic Hyperparameter Defaults. CoRR (2021)
Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren
Meta-learning for symbolic hyperparameter defaults. GECCO Companion (2021)
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren
OpenML Benchmarking Suites. NeurIPS Datasets and Benchmarks (2021)
Mike Huisman, Jan N. van Rijn, Aske Plaat
A survey of deep meta-learning. Artif. Intell. Rev. 54 (6) (2021)
Adrian El Baz, Isabelle Guyon, Zhengying Liu, Jan N. van Rijn, Sébastien Treguer, Joaquin Vanschoren
Advances in MetaDL: AAAI 2021 Challenge and Workshop. MetaDL@AAAI (2021)
Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter
OpenML-Python: an extensible Python API for OpenML. J. Mach. Learn. Res. 22 (2021)
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
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) (2021)
Nelly Rosaura Palacios Salinas, Mitra Baratchi, Jan N. van Rijn, Andreas Vollrath
Automated Machine Learning for Satellite Data: Integrating Remote Sensing Pre-trained Models into AutoML Systems. ECML/PKDD (5) (2021)
Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas C. Müller, Bernd Bischl
Learning multiple defaults for machine learning algorithms. GECCO Companion (2021)
Felix Mohr, Jan N. van Rijn
Fast and Informative Model Selection using Learning Curve Cross-Validation. CoRR (2021)
Mike Huisman, Aske Plaat, Jan N. van Rijn
Stateless Neural Meta-Learning using Second-Order Gradients. CoRR (2021)
Chitsutha Soomlek, Jan N. van Rijn, Marcello M. Bonsangue
Automatic Human-Like Detection of Code Smells. DS (2021)
Mike Huisman, Jan N. van Rijn, Aske Plaat
A Survey of Deep Meta-Learning. CoRR (2020)
Jeannette Shijie Ma, Marcello A. Gómez Maureira, Jan N. van Rijn
Eating Sound Dataset for 20 Food Types and Sound Classification Using Convolutional Neural Networks. ICMI Companion (2020)
Abhinav Sharma, Jan N. van Rijn, Frank Hutter, Andreas Müller
Hyperparameter Importance for Image Classification by Residual Neural Networks. DS (2019)
Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter
OpenML-Python: an extensible Python API for OpenML. CoRR (2019)
Marius Lindauer, Jan N. van Rijn, Lars Kotthoff
The algorithm selection competitions 2015 and 2017. Artif. Intell. 272 (2019)
Noureddin Sadawi, Iván Olier, Joaquin Vanschoren, Jan N. van Rijn, Jeremy Besnard, G. Richard J. Bickerton, Crina Grosan, Larisa N. Soldatova, Ross D. King
Multi-task learning with a natural metric for quantitative structure activity relationship learning. J. Cheminformatics 11 (1) (2019)
Benjamin Strang, Peter van der Putten, Jan N. van Rijn, Frank Hutter
Don't Rule Out Simple Models Prematurely: A Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML. IDA (2018)
Jan N. van Rijn, Frank Hutter
Hyperparameter Importance Across Datasets. KDD (2018)
Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren
The online performance estimation framework: heterogeneous ensemble learning for data streams. Mach. Learn. 107 (1) (2018)
Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas C. Müller, Bernd Bischl
Learning Multiple Defaults for Machine Learning Algorithms. CoRR (2018)
Salisu Mamman Abdulrahman, Pavel Brazdil, Jan N. van Rijn, Joaquin Vanschoren
Speeding up algorithm selection using average ranking and active testing by introducing runtime. Mach. Learn. 107 (1) (2018)
Jan N. van Rijn, Frank W. Takes, Jonathan K. Vis
Computing and Predicting Winning Hands in the Trick-Taking Game of Klaverjas. BNCAI (2018)
Marius Lindauer, Jan N. van Rijn, Lars Kotthoff
The Algorithm Selection Competition Series 2015-17. CoRR (2018)
Jan N. van Rijn, Frank Hutter
An Empirical Study of Hyperparameter Importance Across Datasets. AutoML@PKDD/ECML (2017)
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N. van Rijn, Joaquin Vanschoren
OpenML Benchmarking Suites and the OpenML100. CoRR (2017)
Marius Lindauer, Jan N. van Rijn, Lars Kotthoff
Open Algorithm Selection Challenge 2017: Setup and Scenarios. OASC (2017)
Jan N. van Rijn, Frank Hutter
Hyperparameter Importance Across Datasets. CoRR (2017)
Hendrik Jan Hoogeboom, Walter A. Kosters, Jan N. van Rijn, Jonathan K. Vis
Acyclic Constraint Logic and Games. CoRR (2016)
Martijn J. Post, Peter van der Putten, Jan N. van Rijn
Does Feature Selection Improve Classification? A Large Scale Experiment in OpenML. IDA (2016)
Jan N. van Rijn, Jonathan K. Vis
Endgame Analysis of Dou Shou Qi. CoRR (2016)
Jan N. van Rijn, Frank W. Takes, Jonathan K. Vis
The Complexity of Rummikub Problems. CoRR (2016)
Jan N. van Rijn, Salisu Mamman Abdulrahman, Pavel Brazdil, Joaquin Vanschoren
Fast Algorithm Selection Using Learning Curves. IDA (2015)
Salisu Mamman Abdulrahman, Pavel Brazdil, Jan N. van Rijn, Joaquin Vanschoren
Algorithm Selection via Meta-learning and Sample-based Active Testing. MetaSel@PKDD/ECML (2015)
Jan N. van Rijn, Joaquin Vanschoren
Sharing RapidMiner Workflows and Experiments with OpenML. MetaSel@PKDD/ECML (2015)
Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren
Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams. ICDM (2015)
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl
Taking machine learning research online with OpenML. BigMine (2015)
Jan N. van Rijn, Jonathan K. Vis
Endgame Analysis of DOU SHOU QI. J. Int. Comput. Games Assoc. 37 (2) (2014)
Annelot Bosman, Anna L. Münz, Holger H. Hoos, Jan N. van Rijn
A Preliminary Study to Examining Per-class Performance Bias via Robustness Distributions. SAIV (2014)
Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren
Towards Meta-learning over Data Streams. MetaSel@ECAI (2014)
Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren
Algorithm Selection on Data Streams. Discovery Science (2014)
Hendrik Jan Hoogeboom, Walter A. Kosters, Jan N. van Rijn, Jonathan K. Vis
Acyclic Constraint Logic and Games. J. Int. Comput. Games Assoc. 37 (1) (2014)
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, Luís Torgo
OpenML: networked science in machine learning. CoRR (2014)
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, Luís Torgo
OpenML: networked science in machine learning. SIGKDD Explor. 15 (2) (2013)
Jan N. van Rijn, Bernd Bischl, Luís Torgo, Bo Gao, Venkatesh Umaashankar, Simon Fischer, Patrick Winter, Bernd Wiswedel, Michael R. Berthold, Joaquin Vanschoren
OpenML: A Collaborative Science Platform. ECML/PKDD (3) (2013)