| Minimizers of the empirical risk and risk monotonicity M Loog, T Viering, A Mey Advances in Neural Information Processing Systems, 7478-7487, 2019 | 4 | 2019 |
| Open problem: Monotonicity of learning T Viering, A Mey, M Loog Conference on Learning Theory, 3198-3201, 2019 | 3 | 2019 |
| A brief prehistory of double descent M Loog, T Viering, A Mey, JH Krijthe, DMJ Tax Proceedings of the National Academy of Sciences 117 (20), 10625-10626, 2020 | 2 | 2020 |
| How to manipulate cnns to make them lie: the gradcam case T Viering, Z Wang, M Loog, E Eisemann arXiv preprint arXiv:1907.10901, 2019 | 2 | 2019 |
| Nuclear discrepancy for active learning TJ Viering, JH Krijthe, M Loog arXiv preprint arXiv:1706.02645, 2017 | 1 | 2017 |
| Making Learners (More) Monotone TJ Viering, A Mey, M Loog International Symposium on Intelligent Data Analysis, 535-547, 2020 | | 2020 |
| A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization A Mey, TJ Viering, M Loog International Symposium on Intelligent Data Analysis, 326-338, 2020 | | 2020 |
| Nuclear discrepancy for single-shot batch active learning TJ Viering, JH Krijthe, M Loog Machine Learning 108 (8-9), 1561-1599, 2019 | | 2019 |
| Active Learning by Discrepancy Minimization: A Comparison of Active Learning Methods Motivated by Generalization Bounds TJ Viering | | 2016 |
| Generalization Bound Minimization for Active Learning T Viering, J Krijthe, M Loog Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on …, 0 | | |
Marco LoogDelft University of Technology & University of CopenhagenVerified email at tudelft.nl
