# Sampling Unknown Decision Functions to Build Classifier Copies

arXiv: https://arxiv.org/abs/1910.00237 · HTML: https://mlsanity.com/paper/1910.00237/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2019.

## Built on by (most cited first, 4 of 4)

- [Desiderata for Explainable AI in statistical production systems of the European Central Bank](https://mlsanity.com/paper/2107.08045/) (2021)
- [A Scalable and Efficient Iterative Method for Copying Machine Learning Classifiers](https://mlsanity.com/paper/2302.02667/) (2023)
- [Introducing explainable supervised machine learning into interactive feedback loops for statistical production system](https://mlsanity.com/paper/2202.03212/) (2022)
- [Smoothing the Black-Box: Signed-Distance Supervision for Black-Box Model Copying](https://mlsanity.com/paper/2601.20773/) (2026)

## Abstract (arXiv)

Copies have been proposed as a viable alternative to endow machine learning models with properties and features that adapt them to changing needs. A fundamental step of the copying process is generating an unlabelled set of points to explore the decision behavior of the targeted classifier throughout the input space. In this article we propose two sampling strategies to produce such sets. We validate them in six well-known problems and compare them with two standard methods. We evaluate our proposals in terms of both their accuracy performance and their computational cost.