5 citations · 5 across the 3 of their papers we have counts for
5 papers
Fair Interpretable Representation Learning with Correction Vectors
Mattia Cerrato, Alesia Vallenas Coronel, Marius Köppel +3
Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector whi…
Fair Interpretable Learning via Correction Vectors
Mattia Cerrato, Marius Köppel, Alexander Segner +1
Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector whi…
Fair Group-Shared Representations with Normalizing Flows
Mattia Cerrato, Marius Köppel, Alexander Segner +1
The issue of fairness in machine learning stems from the fact that historical data often displays biases against specific groups of people which have been underprivileged in the re…
Shining Light on the Scotogenic Model: Interplay of Colliders and Cosmology
Sven Baumholzer, Vedran Brdar, Pedro Schwaller +1
In the framework of the scotogenic model, which features radiative generation of neutrino masses, we explore light dark matter scenario. Throughout the paper we chiefly focus on ke…
Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance
Marius Köppel, Alexander Segner, Martin Wagener +3
We present a pairwise learning to rank approach based on a neural net, called DirectRanker, that generalizes the RankNet architecture. We show mathematically that our model is refl…