52 citations · 152 across the 5 of their papers we have counts for
5 papers
Generating collective counterfactual explanations in score-based classification via mathematical optimization
Emilio Carrizosa, Jasone Ramírez-Ayerbe, Dolores Romero Morales
Due to the increasing use of Machine Learning models in high stakes decision making settings, it has become increasingly important to have tools to understand how models arrive at…
Supervised Feature Compression based on Counterfactual Analysis
Veronica Piccialli, Dolores Romero Morales, Cecilia Salvatore
Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning. For a given classifier and an instance classified in an undesired class, it…
Optimal randomized classification trees
Rafael Blanquero, Emilio Carrizosa, Cristina Molero-Río +1
Classification and Regression Trees (CARTs) are off-the-shelf techniques in modern Statistics and Machine Learning. CARTs are traditionally built by means of a greedy procedure, se…
On Clustering Categories of Categorical Predictors in Generalized Linear Models
Emilio Carrizosa, Marcela Galvis Restrepo, Dolores Romero Morales
We propose a method to reduce the complexity of Generalized Linear Models in the presence of categorical predictors. The traditional one-hot encoding, where each category is repres…
Sparsity in Optimal Randomized Classification Trees
Rafael Blanquero, Emilio Carrizosa, Cristina Molero-Río +1
Decision trees are popular Classification and Regression tools and, when small-sized, easy to interpret. Traditionally, a greedy approach has been used to build the trees, yielding…