Alexander Mey - RankMe

24 papers A* 3A 5B 3Journal 13

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