Classifier Derandomization

Classifier derandomization aims to convert probabilistic classifiers, which output probabilities of class membership, into deterministic classifiers that provide definitive classifications while preserving desirable properties of the original model. Current research focuses on developing efficient derandomization techniques that maintain accuracy and fairness, addressing challenges such as cardinality constraints and metric fairness in various applications like combinatorial optimization and image classification. These advancements improve the interpretability, verifiability, and efficiency of machine learning models, impacting both theoretical understanding and practical deployment.