5 citations · 9 across the 2 of their papers we have counts for
2 papers
cs.AI2021★ 5 cited
Transport-based Counterfactual Models
Lucas de Lara, Alberto González-Sanz, Nicholas Asher +2
Counterfactual frameworks have grown popular in machine learning for both explaining algorithmic decisions but also defining individual notions of fairness, more intuitive than typ…
math.ST2021★ 4 cited
A Consistent Extension of Discrete Optimal Transport Maps for Machine Learning Applications
Lucas de Lara, Alberto González-Sanz, Jean-Michel Loubes
Optimal transport maps define a one-to-one correspondence between probability distributions, and as such have grown popular for machine learning applications. However, these maps a…