Alexander Mey

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 22

Top Topics

Supervised Learning

Manifold Regularization

Class Probability Estimation

Top Venues

Publications

Alexander Mey, Rui Manuel Castro
Invariant Causal Prediction with Locally Linear Models. CoRR (2024)
Alexander Mey, Marco Loog
Improved Generalization in Semi-Supervised Learning: A Survey of Theoretical Results. IEEE Trans. Pattern Anal. Mach. Intell. 45 (4) (2023)
Elena Congeduti, Alexander Mey, Frans A. Oliehoek
Loss Bounds for Approximate Influence-Based Abstraction. AAMAS (2021)
Alexander Mey, Frans A. Oliehoek
Environment Shift Games: Are Multiple Agents the Solution, and not the Problem, to Non-Stationarity? AAMAS (2021)
Alexander Mey, Marco Loog
Consistency and Finite Sample Behavior of Binary Class Probability Estimation. AAAI (2021)
Alexander Mey, Tom Julian Viering, Marco Loog
A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization. IDA (2020)
Elena Congeduti, Alexander Mey, Frans A. Oliehoek
Loss Bounds for Approximate Influence-Based Abstraction. CoRR (2020)
Julius von Kügelgen, Alexander Mey, Marco Loog, Bernhard Schölkopf
Semi-supervised learning, causality, and the conditional cluster assumption. UAI (2020)
Alexander Mey
A Note on High-Probability versus In-Expectation Guarantees of Generalization Bounds in Machine Learning. CoRR (2020)
Marco Loog, Tom J. Viering, Alexander Mey, Jesse H. Krijthe, David M. J. Tax
A Brief Prehistory of Double Descent. CoRR (2020)
Tom Julian Viering, Alexander Mey, Marco Loog
Making Learners (More) Monotone. IDA (2020)
Marco Loog, Tom J. Viering, Alexander Mey
Minimizers of the Empirical Risk and Risk Monotonicity. CoRR (2019)
Tom J. Viering, Alexander Mey, Marco Loog
Making Learners (More) Monotone. CoRR (2019)
Julius von Kügelgen, Alexander Mey, Marco Loog
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features. AISTATS (2019)
Tom J. Viering, Alexander Mey, Marco Loog
Open Problem: Monotonicity of Learning. COLT (2019)
Marco Loog, Tom J. Viering, Alexander Mey
Minimizers of the Empirical Risk and Risk Monotonicity. NeurIPS (2019)
Julius von Kügelgen, Marco Loog, Alexander Mey, Bernhard Schölkopf
Semi-Supervised Learning, Causality and the Conditional Cluster Assumption. CoRR (2019)
Alexander Mey, Marco Loog
Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results. CoRR (2019)
Alexander Mey, Tom J. Viering, Marco Loog
A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization. CoRR (2019)
Alexander Mey, Marco Loog
Consistency and Finite Sample Behavior of Binary Class Probability Estimation. CoRR (2019)
Julius von Kügelgen, Alexander Mey, Marco Loog
Semi-Generative Modelling: Domain Adaptation with Cause and Effect Features. CoRR (2018)
Alexander Mey, Marco Loog
A soft-labeled self-training approach. ICPR (2016)