M. Loog, T.J. Viering, A. Mey (2019), Minimizers of the empirical risk and risk monotonicity, In Neural Information Processing Systems.

Y. Zeng, J. C.A. van der Lubbe, M. Loog (2019), Multi-scale convolutional neural network for pixel-wise reconstruction of Van Gogh’s drawings, In Machine Vision and Applications Volume 30 p.1229-1241.

Tom J. Viering, Jesse H. Krijthe, Marco Loog (2019), Nuclear discrepancy for single-shot batch active learning, In Machine Learning Volume 108 p.1561-1599.

Mina Sheikhalishahi, Majid Nateghizad, Fabio Martinelli, Zekeriya Erkin, Marco Loog (2019), On the Statistical Detection of Adversarial Instances over Encrypted Data, Sjouke Mauw, Mauro Conti (Eds.), In Security and Trust Management - 15th International Workshop, STM 2019, Proceedings Volume 11738 p.71-88.

Soufiane Mourragui, Marco Loog, Mark A. van der Wiel, Marcel Reinders, Lodewyk Wessels (2019), PRECISE: A domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors, In Bioinformatics Volume 35 p.i510-i519.

Wouter M. Kouw, Jesse H. Krijthe, Marco Loog (2019), Robust Importance-Weighted Cross-Validation under Sample Selection Bias, In 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019 p.1-6.

Yazhou Yang, Marco Loog (2019), Single shot active learning using pseudo annotators, In Pattern Recognition Volume 89 p.22-31.

Yazhou Yang, Marco Loog (2018), A benchmark and comparison of active learning for logistic regression, In Pattern Recognition Volume 83 p.401-415.

Yazhou Yang, Marco Loog (2018), A variance maximization criterion for active learning, In Pattern Recognition Volume 78 p.358-370.

M. van Stralen, Y. Zhou, P.J. Wozny, P.R. Seevinck, M. Loog (2018), Contextual loss functions for optimization of convolutional neural networks generating pseudo CTs from MRI, E.D. Angelini, B.A. Landman (Eds.), In Medical Imaging 2018 p.105741N-1 - 105741N-6, SPIE.