4 papers
Data-Driven Random Projection and Screening for High-Dimensional Generalized Linear Models
Roman Parzer, Peter Filzmoser, Laura Vana-Gür
We address the challenge of correlated predictors in high-dimensional GLMs, where regression coefficients range from sparse to dense, by proposing a data-driven random projection m…
Cellwise robust and sparse principal component analysis
Pia Pfeiffer, Laura Vana-Gür, Peter Filzmoser
A first proposal of a sparse and cellwise robust PCA method is presented. Robustness to single outlying cells in the data matrix is achieved by substituting the squared loss functi…
Automated Model Selection for Generalized Linear Models
Benjamin Schwendinger, Florian Schwendinger, Laura Vana-Gür
In this paper, we show how mixed-integer conic optimization can be used to combine feature subset selection with holistic generalized linear models to fully automate the model sele…
Bayesian Machine Learning meets Formal Methods: An application to spatio-temporal data
Laura Vana, Ennio Visconti, Laura Nenzi +2
We propose an interdisciplinary framework that combines Bayesian predictive inference, a well-established tool in Machine Learning, with Formal Methods rooted in the computer scien…