Alexander Mey
Title
Cited by
Cited by
Year
Improvability through semi-supervised learning: a survey of theoretical results
A Mey, M Loog
arXiv preprint arXiv:1908.09574, 2019
52019
Minimizers of the empirical risk and risk monotonicity
M Loog, T Viering, A Mey
Advances in Neural Information Processing Systems, 7478-7487, 2019
42019
Open problem: Monotonicity of learning
T Viering, A Mey, M Loog
Conference on Learning Theory, 3198-3201, 2019
32019
A soft-labeled self-training approach
A Mey, M Loog
2016 23rd International Conference on Pattern Recognition (ICPR), 2604-2609, 2016
32016
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
22020
Semi-supervised learning, causality and the conditional cluster assumption
J von Kügelgen, A Mey, M Loog, B Schölkopf
arXiv preprint arXiv:1905.12081, 2019
12019
Semi-generative modelling: Covariate-shift adaptation with cause and effect features
J Kügelgen, A Mey, M Loog
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
12019
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
Assumptions & Expectations in Semi-Supervised Machine Learning
A Mey
2020
Consistency and Finite Sample Behavior of Binary Class Probability Estimation
A Mey, M Loog
arXiv preprint arXiv:1908.11823, 2019
2019
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Articles 1–11