52 citations · 152 across the 17 of their papers we have counts for
5 papers · 1 filter
LOSDD: Leave-Out Support Vector Data Description for Outlier Detection
Daniel Boiar, Thomas Liebig, Erich Schubert
Support Vector Machines have been successfully used for one-class classification (OCSVM, SVDD) when trained on clean data, but they work much worse on dirty data: outliers present…
Forecasting Unobserved Node States with spatio-temporal Graph Neural Networks
Andreas Roth, Thomas Liebig
Forecasting future states of sensors is key to solving tasks like weather prediction, route planning, and many others when dealing with networks of sensors. But complete spatial co…
Certified Data Removal in Sum-Product Networks
Alexander Becker, Thomas Liebig
Data protection regulations like the GDPR or the California Consumer Privacy Act give users more control over the data that is collected about them. Deleting the collected data is…
Evaluating Machine Unlearning via Epistemic Uncertainty
Alexander Becker, Thomas Liebig
There has been a growing interest in Machine Unlearning recently, primarily due to legal requirements such as the General Data Protection Regulation (GDPR) and the California Consu…
Transforming PageRank into an Infinite-Depth Graph Neural Network
Andreas Roth, Thomas Liebig
Popular graph neural networks are shallow models, despite the success of very deep architectures in other application domains of deep learning. This reduces the modeling capacity a…