A survey of deep meta-learning von Huisman, Mike, van Rijn, Jan N., Plaat, Aske

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Metalearning: Applications to Automated Machine Learning and Data Mining von Brazdil, Pavel, van Rijn, Jan N, Soares, Carlos, Vanschoren, Joaquin

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Fast and Informative Model Selection Using Learning Curve Cross-Validation von Mohr, Felix, van Rijn, Jan N.

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The online performance estimation framework: heterogeneous ensemble learning for data streams von van Rijn, Jan N., Holmes, Geoffrey, Pfahringer, Bernhard, Vanschoren, Joaquin

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The algorithm selection competitions 2015 and 2017 von Lindauer, Marius, van Rijn, Jan N., Kotthoff, Lars

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Multi-task learning with a natural metric for quantitative structure activity relationship learning von Sadawi, Noureddin, Olier, Ivan, Vanschoren, Joaquin, van Rijn, Jan N., Besnard, Jeremy, Bickerton, Richard, Grosan, Crina, Soldatova, Larisa, King, Ross D.

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Understanding transfer learning and gradient-based meta-learning techniques von Huisman, Mike, Plaat, Aske, van Rijn, Jan N.

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Stateless neural meta-learning using second-order gradients von Huisman, Mike, Plaat, Aske, van Rijn, Jan N.

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

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Speeding up algorithm selection using average ranking and active testing by introducing runtime von Abdulrahman, Salisu Mamman, Brazdil, Pavel, van Rijn, Jan N., Vanschoren, Joaquin

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Are LSTMs good few-shot learners? von Huisman, Mike, Moerland, Thomas M., Plaat, Aske, van Rijn, Jan N.

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Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio von König, Matthias, Hoos, Holger H., Rijn, Jan N. van

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Hyperparameter importance and optimization of quantum neural networks across small datasets von Moussa, Charles, Patel, Yash J., Dunjko, Vedran, Bäck, Thomas, van Rijn, Jan N.

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The Bigger Fish: A Comparison of Meta-Learning QSAR Models on Low-Resourced Aquatic Toxicity Regression Tasks von Schlender, Thalea, Viljanen, Markus, van Rijn, Jan N, Mohr, Felix, Peijnenburg, Willie Jgm, Hoos, Holger H, Rorije, Emiel, Wong, Albert

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Automated machine learning: past, present and future von Baratchi, Mitra, Wang, Can, Limmer, Steffen, van Rijn, Jan N., Hoos, Holger, Bäck, Thomas, Olhofer, Markus

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Learning Curve Extrapolation Methods Across Extrapolation Settings von Kielhöfer, Lionel, Mohr, Felix, van Rijn, Jan N.

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

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Don't Rule Out Simple Models Prematurely: A Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML von Strang, Benjamin, Putten, Peter van der, Rijn, Jan N. van, Hutter, Frank

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OpenML-Python: an extensible Python API for OpenML von Feurer, Matthias, van Rijn, Jan N., Kadra, Arlind, Gijsbers, Pieter, Mallik, Neeratyoy, Ravi, Sahithya, Muller, Andreas, Vanschoren, Joaquin, Hutter, Frank

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Guest Editorial: AutoML for Nonstationary Data von Cheng, Ran, Escalante, Hugo Jair, Tu, Wei-Wei, Rijn, Jan N. Van, Wang, Shuo, Yang, Yun

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