11 citations · 11 across the 4 of their papers we have counts for
6 papers · 1 filter
A Scalable and Efficient Iterative Method for Copying Machine Learning Classifiers
Nahuel Statuto, Irene Unceta, Jordi Nin +1
Differential replication through copying refers to the process of replicating the decision behavior of a machine learning model using another model that possesses enhanced features…
Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems
Carlos Mougan, David Masip, Jordi Nin +1
Regression problems have been widely studied in machinelearning literature resulting in a plethora of regression models and performance measures. However, there are few techniques…
Differential Replication in Machine Learning
Irene Unceta, Jordi Nin, Oriol Pujol
When deployed in the wild, machine learning models are usually confronted with data and requirements that constantly vary, either because of changes in the generating distribution…
Sampling Unknown Decision Functions to Build Classifier Copies
Irene Unceta, Diego Palacios, Jordi Nin +1
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…
Copying Machine Learning Classifiers
Irene Unceta, Jordi Nin, Oriol Pujol
We study model-agnostic copies of machine learning classifiers. We develop the theory behind the problem of copying, highlighting its differences with that of learning, and propose…
Towards Global Explanations for Credit Risk Scoring
Irene Unceta, Jordi Nin, Oriol Pujol
In this paper we propose a method to obtain global explanations for trained black-box classifiers by sampling their decision function to learn alternative interpretable models. The…