Copying Machine Learning Classifiers

Irene Unceta, Jordi Nin, Oriol Pujol

Research output: Indexed journal article Articlepeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Article number9181566
Pages (from-to)160268-160284
Number of pages17
JournalIEEE Access
Publication statusPublished - 2020


  • Applied machine learning
  • classification
  • copying
  • differential replication
  • fidelity


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