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Alexander Mey
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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
412020
Minimizers of the empirical risk and risk monotonicity
M Loog, T Viering, A Mey
Advances in Neural Information Processing Systems 32, 2019
182019
Improvability through semi-supervised learning: A survey of theoretical results
A Mey, M Loog
arXiv preprint arXiv:1908.09574, 2019
152019
Open problem: Monotonicity of learning
T Viering, A Mey, M Loog
Conference on Learning Theory, 3198-3201, 2019
152019
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
122019
Semi-supervised learning, causality, and the conditional cluster assumption
J Kügelgen, A Mey, M Loog, B Schölkopf
Conference on Uncertainty in Artificial Intelligence, 1-10, 2020
112020
A soft-labeled self-training approach
A Mey, M Loog
2016 23rd International Conference on Pattern Recognition (ICPR), 2604-2609, 2016
92016
Loss bounds for approximate influence-based abstraction
E Congeduti, A Mey, FA Oliehoek
arXiv preprint arXiv:2011.01788, 2020
82020
Making learners (more) monotone
TJ Viering, A Mey, M Loog
International Symposium on Intelligent Data Analysis, 535-547, 2020
62020
Consistency and Finite Sample Behavior of Binary Class Probability Estimation
A Mey, M Loog
Proceedings of the AAAI Conference on Artificial Intelligence 35 (10), 8967-8974, 2021
12021
Environment Shift Games: Are Multiple Agents the Solution, and not the Problem, to Non-Stationarity?
A Mey, FA Oliehoek
12021
A note on high-probability versus in-expectation guarantees of generalization bounds in machine learning
A Mey
arXiv preprint arXiv:2010.02576, 2020
12020
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
12020
Improved Generalization in Semi-Supervised Learning: A Survey of Theoretical Results
A Mey, M Loog
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
2022
AboutInfluence'
FA Oliehoek, E Congeduti, A Czechowski, J He, A Mey, RAN Starre, ...
2022
Assumptions & Expectations in Semi-Supervised Machine Learning
A Mey
2020
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