6 papers
Mixed-Integer Linear Optimization for Semi-Supervised Optimal Classification Trees
Jan Pablo Burgard, Maria Eduarda Pinheiro, Martin Schmidt
Decision trees are one of the most popular methods for solving classification problems, mainly because of their good interpretability properties. Moreover, due to advances in recen…
Household size can explain 40% of the variance in cumulative COVID-19 incidence across Europe
Seba Contreras, Philipp Dönges, Maciej Filinski +8
Household size impacts the spread of respiratory infectious diseases: Larger households tend to boost transmission by acquiring external infections more frequently and subsequently…
Mixed-Integer Linear Optimization for Cardinality-Constrained Random Forests
Jan Pablo Burgard, Maria Eduarda Pinheiro, Martin Schmidt
Random forests are among the most famous algorithms for solving classification problems, in particular for large-scale data sets. Considering a set of labeled points and several de…
FairML: A Julia Package for Fair Classification
Jan Pablo Burgard, João Vitor Pamplona
In this paper, we propose FairML.jl, a Julia package providing a framework for fair classification in machine learning. In this framework, the fair learning process is divided into…
Fair Generalized Linear Mixed Models
Jan Pablo Burgard, João Vitor Pamplona
When using machine learning for automated prediction, it is important to account for fairness in the prediction. Fairness in machine learning aims to ensure that biases in the data…
Fair Mixed Effects Support Vector Machine
Jan Pablo Burgard, João Vitor Pamplona
To ensure unbiased and ethical automated predictions, fairness must be a core principle in machine learning applications. Fairness in machine learning aims to mitigate biases prese…