| 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 | 60 | 2020 |
| Minimizers of the empirical risk and risk monotonicity M Loog, T Viering, A Mey Advances in Neural Information Processing Systems 32, 2019 | 24 | 2019 |
| 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 | 23 | 2020 |
| Open problem: Monotonicity of learning T Viering, A Mey, M Loog Conference on Learning Theory, 3198-3201, 2019 | 22 | 2019 |
| Improvability through semi-supervised learning: A survey of theoretical results A Mey, M Loog arXiv preprint arXiv:1908.09574, 2019 | 19 | 2019 |
| 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 | 13 | 2019 |
| A soft-labeled self-training approach A Mey, M Loog 2016 23rd International Conference on Pattern Recognition (ICPR), 2604-2609, 2016 | 11 | 2016 |
| Making learners (more) monotone TJ Viering, A Mey, M Loog International Symposium on Intelligent Data Analysis, 535-547, 2020 | 9 | 2020 |
| Loss bounds for approximate influence-based abstraction E Congeduti, A Mey, FA Oliehoek arXiv preprint arXiv:2011.01788, 2020 | 7 | 2020 |
| Improved Generalization in Semi-Supervised Learning: A Survey of Theoretical Results A Mey, M Loog IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (4), 4747-4767, 2022 | 5 | 2022 |
| 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 | 2 | 2021 |
| Environment Shift Games: Are Multiple Agents the Solution, and not the Problem, to Non-Stationarity? A Mey, FA Oliehoek | 1 | 2021 |
| A note on high-probability versus in-expectation guarantees of generalization bounds in machine learning A Mey arXiv preprint arXiv:2010.02576, 2020 | 1 | 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 | 1 | 2020 |
| A Survey on Scenario Theory, Complexity, and Compression-Based Learning and Generalization R Rocchetta, A Mey, FA Oliehoek IEEE Transactions on Neural Networks and Learning Systems, 2023 | | 2023 |
| Exploring the link between scenario theory, complexity-and compression-based learning R Rocchetta, A Mey, FA Oliehoek TechRxiv, 2022 | | 2022 |
| Causal Discovery in Time Series Data Using Causally Invariant Locally Linear Models A Mey A causal view on dynamical systems, NeurIPS 2022 workshop, 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 |
