7 citations · 10 across the 5 of their papers we have counts for
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cs.LG2024★ 2 cited
Spatio-Spectral Graph Neural Networks
Simon Geisler, Arthur Kosmala, Daniel Herbst +1
Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for learning on graph-structured data. However, key limitations of l-step MPGNNs are that their "receptive fi…
cs.CR2024
Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification
Jan Schuchardt, Mihail Stoian, Arthur Kosmala +1
Amplification by subsampling is one of the main primitives in machine learning with differential privacy (DP): Training a model on random batches instead of complete datasets resul…