Search Results for author: Rongzhe Wei

Found 5 papers, 2 papers with code

Learning Scalable Structural Representations for Link Prediction with Bloom Signatures

no code implementations28 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.

Link Prediction

On the Inherent Privacy Properties of Discrete Denoising Diffusion Models

no code implementations24 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.

Denoising Privacy Preserving

Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective

1 code implementation22 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.

Bayesian Inference Node Classification

SLA$^2$P: Self-supervised Anomaly Detection with Adversarial Perturbation

1 code implementation25 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.

Pseudo Label Self-Supervised Anomaly Detection +3

DWMD: Dimensional Weighted Orderwise Moment Discrepancy for Domain-specific Hidden Representation Matching

no code implementations18 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.

Transfer Learning Unsupervised Domain Adaptation +1

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