39 citations · 78 across the 12 of their papers we have counts for
Showing 2019Show all
3 papers · 1 filter
cs.LG2019★ 1 cited
Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability
José Mena, Oriol Pujol, Jordi Vitrià
Nowadays, the use of machine learning models is becoming a utility in many applications. Companies deliver pre-trained models encapsulated as application programming interfaces (AP…
cs.LG2019
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…
cs.LG2019
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…