A. Mey, M. Loog (2021), Consistency and Finite Sample Behavior of Binary Class Probability Estimation, In 35th aaai conference on artificial intelligence 33rd conference on innovative applications of artificial intelligence the eleventh symposium on educational advances in artificial intelligence p.8967-8974, Association for the Advancement of Artificial Intelligence (AAAI).
A. Mey, F.A. Oliehoek (2021), Environment Shift Games: Are Multiple Agents the Solution, and not the Problem, to Non-Stationarity?, In AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems p.23-27, International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).
E. Congeduti, A. Mey, F.A. Oliehoek (2021), Loss Bounds for Approximate Influence-Based Abstraction.
Alexander Mey, Tom Julian Viering, Marco Loog (2020), A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization, Michael R. Berthold, Ad Feelders, Georg Krempl (Eds.), In Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Proceedings Volume 12080 p.326-338, Springer Open.
Alex Mey (2020), Assumptions & Expectations in Semi-Supervised Machine Learning, PhD Thesis Delft University of Technology.
Tom Julian Viering, Alexander Mey, Marco Loog (2020), Making Learners (More) Monotone, Michael R. Berthold, Ad Feelders, Georg Krempl (Eds.), In Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Proceedings Volume 12080 p.535-547, Springer Open.
M. Loog, T.J. Viering, A. Mey (2019), Minimizers of the empirical risk and risk monotonicity, In Neural Information Processing Systems.
Alexander Mey, Marco Loog (2016), A soft-labeled self-training approach, In 2016 23rd International Conference on Pattern Recognition (ICPR) p.2604-2609, IEEE .