1 citations · 2 across the 6 of their papers we have counts for
7 papers
Misclassification Rate and Privacy-Utility Trade-offs in Graph Convolutional Networks via Subsampling Stability
Yexin Zhang, Zhongtian Ma, Qiaosheng Zhang +1
We study differential privacy (DP) in Graph Convolutional Networks (GCNs) through the framework of \textit{subsampling stability}. We derive upper bounds on the misclassification r…
Gating and Learnable Temperature for Graph Attention Mechanisms
Zhongtian Ma, Hao Wu, Yexin Zhang +3
Graph attention networks learn neighbor importance through data-dependent coefficients, but standard layers lack explicit control over unreliable feature dimensions and use fixed s…
SafeWork-R1: Coevolving Safety and Intelligence under the AI-45 Law
Shanghai AI Lab, :, Yicheng Bao +115
We introduce SafeWork-R1, a cutting-edge multimodal reasoning model that demonstrates the coevolution of capabilities and safety. It is developed by our proposed SafeLadder framewo…
AutoLayout: Closed-Loop Layout Synthesis via Slow-Fast Collaborative Reasoning
Weixing Chen, Dafeng Chi, Yang Liu +7
The automated generation of layouts is vital for embodied intelligence and autonomous systems, supporting applications from virtual environment construction to home robot deploymen…
Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models
Zhongtian Ma, Qiaosheng Zhang, Bocheng Zhou +3
Despite the growing popularity of graph attention mechanisms, their theoretical understanding remains limited. This paper aims to explore the conditions under which these mechanism…
Gradient Testing and Estimation by Comparisons
Xiwen Tao, Chenyi Zhang, Helin Wang +2
We study gradient testing and gradient estimation of smooth functions using only a comparison oracle that, given two points, indicates which one has the larger function value. For…
Community Detection in the Multi-View Stochastic Block Model
Yexin Zhang, Zhongtian Ma, Qiaosheng Zhang +2
This paper considers the problem of community detection on multiple potentially correlated graphs from an information-theoretical perspective. We first put forth a random graph mod…