11 citations · 22 across the 5 of their papers we have counts for
5 papers
Private and Fair Machine Learning: Revisiting the Disparate Impact of Differentially Private SGD
Lea Demelius, Dominik Kowald, Simone Kopeinik +2
Differential privacy (DP) is a prominent method for protecting information about individuals during data analysis. Training neural networks with differentially private stochastic g…
Multistakeholder Fairness in Tourism: What can Algorithms learn from Tourism Management?
Peter Muellner, Anna Schreuer, Simone Kopeinik +2
Algorithmic decision-support systems, i.e., recommender systems, are popular digital tools that help tourists decide which places and attractions to explore. However, algorithms of…
Exploring the Effect of Context-Awareness and Popularity Calibration on Popularity Bias in POI Recommendations
Andrea Forster, Simone Kopeinik, Denic Helic +2
Point-of-interest (POI) recommender systems help users discover relevant locations, but their effectiveness is often compromised by popularity bias, which disadvantages less popula…
Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers
Harald Semmelrock, Tony Ross-Hellauer, Simone Kopeinik +4
Many research fields are currently reckoning with issues of poor levels of reproducibility. Some label it a "crisis", and research employing or building Machine Learning (ML) model…
Take the aTrain. Introducing an Interface for the Accessible Transcription of Interviews
Armin Haberl, Jürgen Fleiß, Dominik Kowald +1
aTrain is an open-source and offline tool for transcribing audio data in multiple languages with CPU and NVIDIA GPU support. It is specifically designed for researchers using quali…