We are excited to announce that four papers have been accepted at the SIAM International Conference on Data Mining (SDM 2023) with an acceptance rate of ~27.4%.
- "A Temporal Graphlet Kernel for Classifying Dissemination in Evolving Networks" by Lutz Oettershagen, Nils M. Kriege, Claude Jordan, and Petra Mutzel,
- "Adaptive Precision Training (AdaPT): A dynamic quantized training approach for DNNs" by Lorenz Kummer, Kevin Sidak, Tabea Reichmann, and Wilfried Gansterer,
- "Influence without Authority: Maximizing Information Coverage in Hypergraphs" by Peiyan Li, Honglian Wang, Kai Li, and Christian Böhm, and
- "Extension of the Dip-test Repertoire - Efficient and Differentiable p-value Calculation for Clustering" by Lena Bauer, Collin Leiber, Christian Böhm, and Claudia Plant