Copying Machine Learning Classifiers

Producción científica: Contribución a una conferenciaContribuciónrevisión exhaustiva


We study copying of machine learning classifiers, an agnostic technique to replicate the decision behavior of any classifier. We develop the theory behind the problem of copying, highlighting its properties, and propose a framework to copy the decision behavior of any classifier using no prior knowledge of its parameters or training data distribution. We validate this framework through extensive experiments using data from a series of well-known problems. To further validate this concept, we use three different use cases where desiderata such as interpretability, fairness or productivization constrains need to be addressed. Results show that copies can be exploited to enhance existing solutions and improve them adding new features and characteristics.
Idioma originalInglés
EstadoPublicada - 2022
EventoInternational Conference on AI and Financial Services - Banco de España, Madrid
Duración: 1 ene 2022 → …


TallerInternational Conference on AI and Financial Services
Período1/01/22 → …


Profundice en los temas de investigación de 'Copying Machine Learning Classifiers'. En conjunto forman una huella única.

Citar esto