van Rijn, Jan N.

22 publications

ICLRW 2025 Data Efficient Pre-Training for Language Models: An Empirical Study of Compute Efficiency and Linguistic Competence Andreas Paraskeva, Max Johannes van Duijn, Maarten de Rijke, Suzan Verberne, Jan N. van Rijn

JAIR 2025 Robustness Distributions in Neural Network Verification Annelot W. Bosman, Aaron Berger, Holger H. Hoos, Jan N. van Rijn

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

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

JMLR 2024 Critically Assessing the State of the Art in Neural Network Verification Matthias König, Annelot W. Bosman, Holger H. Hoos, Jan N. van Rijn

MLJ 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

MLJ 2024 Learning Curves for Decision Making in Supervised Machine Learning: A Survey Felix Mohr, Jan N. van Rijn

ICMLW 2024 Resource-Constrained Neural Architecture Search on Language Models: A Case Study Andreas Paraskeva, Joao Pedro Reis, Suzan Verberne, Jan N. van Rijn

MLJ 2024 Subspace Adaptation Prior for Few-Shot Learning Mike Huisman, Aske Plaat, Jan N. van Rijn

MLJ 2024 Understanding Transfer Learning and Gradient-Based Meta-Learning Techniques Mike Huisman, Aske Plaat, Jan N. van Rijn

MLJ 2023 Are LSTMs Good Few-Shot Learners? Mike Huisman, Thomas M. Moerland, Aske Plaat, Jan N. van Rijn

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

NeurIPS 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

MLJ 2022 Speeding up Neural Network Robustness Verification via Algorithm Configuration and an Optimised Mixed Integer Linear Programming Solver Portfolio Matthias König, Holger H. Hoos, Jan N. van Rijn

MLJ 2022 Stateless Neural Meta-Learning Using Second-Order Gradients Mike Huisman, Aske Plaat, Jan N. van Rijn

NeurIPSW 2021 A Preliminary Study on the Feature Representations of Transfer Learning and Gradient-Based Meta-Learning Techniques Mike Huisman, Jan N. van Rijn, Aske Plaat

ECML-PKDD 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

MLOSS 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, Joaquin Vanschoren, Frank Hutter

ICMLW 2021 Towards Model Selection Using Learning Curve Cross-Validation Felix Mohr, Jan N. van Rijn

MLJ 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

MLJ 2018 The Online Performance Estimation Framework: Heterogeneous Ensemble Learning for Data Streams Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren

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