no code implementations • 28 Dec 2023 • Tianyi Zhang, Haoteng Yin, Rongzhe Wei, Pan Li, Anshumali Shrivastava
We further show that any type of neighborhood overlap-based heuristic can be estimated by a neural network that takes Bloom signatures as input.
no code implementations • 24 Oct 2023 • Rongzhe Wei, Eleonora Kreačić, Haoyu Wang, Haoteng Yin, Eli Chien, Vamsi K. Potluru, Pan Li
Focusing on per-instance differential privacy (pDP), our framework elucidates the potential privacy leakage for each data point in a given training dataset, offering insights into data preprocessing to reduce privacy risks of the synthetic dataset generation via DDMs.
1 code implementation • 22 Jul 2022 • Rongzhe Wei, Haoteng Yin, Junteng Jia, Austin R. Benson, Pan Li
Graph neural networks (GNNs) have shown superiority in many prediction tasks over graphs due to their impressive capability of capturing nonlinear relations in graph-structured data.
1 code implementation • 25 Nov 2021 • Yizhou Wang, Can Qin, Rongzhe Wei, Yi Xu, Yue Bai, Yun Fu
Next we add adversarial perturbation to the transformed features to decrease their softmax scores of the predicted labels and design anomaly scores based on the predictive uncertainties of the classifier on these perturbed features.
no code implementations • 18 Jul 2020 • Rongzhe Wei, Fa Zhang, Bo Dong, Qinghua Zheng
Our metric function takes advantage of a series for high-order moment alignment, and we theoretically prove that our DWMD metric function is error-free, which means that it can strictly reflect the distribution differences between domains and is valid without any feature distribution assumption.