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Representation and learning of context-dependent choice functions / Karlson Pfannschmidt ; 1st Reviewer: Prof. Dr. Eyke Hüllermeier, 2nd Reviewer: Prof. Dr. Heike Trautmann. Paderborn, 2024
Content
Titlepage
Abstract
Contents
Introduction
Research Questions and Objectives
Contributions and Potential Impact
Thesis Outline and Reading Order
Co-Author Contribution Statement
Other Publications
Preliminaries
Foundations of Preferences
Introduction to Notation
Classical Preference Axioms
Context Effects
Preference Modeling
Concepts from Machine Learning
Generalization
Hyperparameter Optimization
Neural Networks and Inductive Biases
Preference Learning
Preference Learning Settings
Evaluation Measures
Solution Approaches
Three Approaches to Modeling Context-Dependent Preferences
Utility-Based Preferences
Generalized Utility
Generating Preferences from Utilities
Decomposing Generalized Utility
Generalized Utility using Neural Networks
Empirical Evaluation
Conclusion and Future Work
Subset Choices using Pareto-Embeddings
Modeling Choice
Pareto-Embeddings
Empirical Evaluation
Conclusion and Future Work
Conclusion and Future Work
Summary of Key Findings
Contextualization and Impact
Future Research Directions
Concluding Statement
Appendix
Glossary
Symbols
Appendix
Additional Experimental Details
Design of the Generalization Experiment
Synthetic Datasets
Real-World Datasets
Detailed Experimental Results
List of Publications
Bibliography
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