Alexander Mey

According to our database, Alexander Mey authored at least 18 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A Survey on Scenario Theory, Complexity, and Compression-Based Learning and Generalization.

Roberto Rocchetta, Alexander Mey, Frans A. Oliehoek

IEEE Trans. Neural Networks Learn. Syst., December, 2024

Invariant Causal Prediction with Locally Linear Models.

Alexander Mey, Rui Manuel Castro

CoRR, 2024

Invariant Causal Prediction with Local Models.

Alexander Mey, Rui Manuel Castro

Proceedings of the Uncertainty in Artificial Intelligence, 2024

2023

Improved Generalization in Semi-Supervised Learning: A Survey of Theoretical Results.

Alexander Mey, Marco Loog

IEEE Trans. Pattern Anal. Mach. Intell., April, 2023

2021

Environment Shift Games: Are Multiple Agents the Solution, and not the Problem, to Non-Stationarity?

Alexander Mey, Frans A. Oliehoek

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

Loss Bounds for Approximate Influence-Based Abstraction.

Elena Congeduti, Alexander Mey, Frans A. Oliehoek

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

Consistency and Finite Sample Behavior of Binary Class Probability Estimation.

Alexander Mey, Marco Loog

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

A Note on High-Probability versus In-Expectation Guarantees of Generalization Bounds in Machine Learning.

Alexander Mey

CoRR, 2020

A Brief Prehistory of Double Descent.

Marco Loog, Tom J. Viering, Alexander Mey, Jesse H. Krijthe, David M. J. Tax

CoRR, 2020

Semi-supervised learning, causality, and the conditional cluster assumption.

Julius von Kügelgen, Alexander Mey, Marco Loog, Bernhard Schölkopf

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Making Learners (More) Monotone.

Tom Julian Viering, Alexander Mey, Marco Loog

Proceedings of the Advances in Intelligent Data Analysis XVIII, 2020

A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization.

Alexander Mey, Tom Julian Viering, Marco Loog

Proceedings of the Advances in Intelligent Data Analysis XVIII, 2020

2019

Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results.

Alexander Mey, Marco Loog

CoRR, 2019

Minimizers of the Empirical Risk and Risk Monotonicity.

Marco Loog, Tom J. Viering, Alexander Mey

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Open Problem: Monotonicity of Learning.

Tom J. Viering, Alexander Mey, Marco Loog

Proceedings of the Conference on Learning Theory, 2019

Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features.

Julius von Kügelgen, Alexander Mey, Marco Loog

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Semi-Generative Modelling: Domain Adaptation with Cause and Effect Features.

Julius von Kügelgen, Alexander Mey, Marco Loog

CoRR, 2018

2016

A soft-labeled self-training approach.

Alexander Mey, Marco Loog

Proceedings of the 23rd International Conference on Pattern Recognition, 2016