16 citations · 35 across the 9 of their papers we have counts for
9 papers
Can machine learning solve the challenge of adaptive learning and the individualization of learning paths? A field experiment in an online learning platform
Tim Klausmann, Marius Köppel, Daniel Schunk +1
The individualization of learning contents based on digital technologies promises large individual and social benefits. However, it remains an open question how this individualizat…
Google Topics as a way out of the cookie dilemma?
Marius Köppel, Jan-Philipp Muttach, Gerrit Hornung
The paper discusses the legal requirements and implications of the processing of information and personal data for advertising purposes, particularly in the light of the "Planet49"…
10 Years of Fair Representations: Challenges and Opportunities
Mattia Cerrato, Marius Köppel, Philipp Wolf +1
Fair Representation Learning (FRL) is a broad set of techniques, mostly based on neural networks, that seeks to learn new representations of data in which sensitive or undesired in…
Invariant Representations with Stochastically Quantized Neural Networks
Mattia Cerrato, Marius Köppel, Roberto Esposito +1
Representation learning algorithms offer the opportunity to learn invariant representations of the input data with regard to nuisance factors. Many authors have leveraged such stra…
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…
The Mu3e Data Acquisition
Heiko Augustin, Niklaus Berger, Alessandro Bravar +25
The Mu3e experiment aims to find or exclude the lepton flavour violating decay with a sensitivity of one in 10 muon decays. The first phase of the experim…
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…
Performance of the large scale HV-CMOS pixel sensor MuPix8
H. Augustin, N. Berger, C. Blattgerste +26
The Mu3e experiment is searching for the charged lepton flavour violating decay , aiming for an ultimate sensitivity of one in decays. In an…