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cs.LG2025
Preserving Node-level Privacy in Graph Neural Networks
Zihang Xiang, Tianhao Wang, Di Wang
Differential privacy (DP) has seen immense applications in learning on tabular, image, and sequential data where instance-level privacy is concerned. In learning on graphs, contras…
cs.LG2025
Revisiting Differentially Private Hyper-parameter Tuning
Zihang Xiang, Tianhao Wang, Chenglong Wang +1
We study the application of differential privacy in hyper-parameter tuning, a crucial process in machine learning involving selecting the best hyper-parameter from several candidat…