4 citations · 4 across the 10 of their papers we have counts for
10 papers
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…
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…
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…
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…
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…
Mixed-Integer Quadratic Optimization and Iterative Clustering Techniques for Semi-Supervised Support Vector Machines
Jan Pablo Burgard, Maria Eduarda Pinheiro, Martin Schmidt
Among the most famous algorithms for solving classification problems are support vector machines (SVMs), which find a separating hyperplane for a set of labeled data points. In som…
Riemannian Optimization for Variance Estimation in Linear Mixed Models
Lena Sembach, Jan Pablo Burgard, Volker H. Schulz
Variance parameter estimation in linear mixed models is a challenge for many classical nonlinear optimization algorithms due to the positive-definiteness constraint of the random e…
Regional estimates of reproduction numbers with application to COVID-19
Jan Pablo Burgard, Stefan Heyder, Thomas Hotz +1
In the last year many public health decisions were based on real-time monitoring the spread of the ongoing COVID-19 pandemic. For this one often considers the reproduction number w…
A Riemannian Newton Trust-Region Method for Fitting Gaussian Mixture Models
Lena Sembach, Jan Pablo Burgard, Volker H. Schulz
Gaussian Mixture Models are a powerful tool in Data Science and Statistics that are mainly used for clustering and density approximation. The task of estimating the model parameter…