6 citations · 20 across the 11 of their papers we have counts for
13 papers
Guided Diffusion for Trustworthy Synthetic Text Generation
Qi Pang, Hanshen Xiao, Andre Manoel +4
The rapid evolution of Large Language Models is fueled by massive datasets, yet this reliance poses severe privacy risks, as models can inadvertently memorize and even directly out…
Codetta: High-Capacity, Keyless, and Undetectable Multi-Agent Collusion
Qi Pang, Virginia Smith, Wenting Zheng
Multi-agent systems built on large language models (LLMs) are increasingly deployed in high-stakes settings such as finance, healthcare, and software engineering, where agents coor…
BadMoE: Backdooring Mixture-of-Experts LLMs via Optimizing Routing Triggers and Infecting Dormant Experts
Qingyue Wang, Qi Pang, Xixun Lin +2
Mixture-of-Experts (MoE) have emerged as a powerful architecture for large language models (LLMs), enabling efficient scaling of model capacity while maintaining manageable computa…
Unveiling Hidden DNN Defects with Decision-Based Metamorphic Testing
Yuanyuan Yuan, Qi Pang, Shuai Wang
Contemporary DNN testing works are frequently conducted using metamorphic testing (MT). In general, de facto MT frameworks mutate DNN input images using semantics-preserving mutati…
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees
Banghua Zhu, Lun Wang, Qi Pang +4
We propose Byzantine-robust federated learning protocols with nearly optimal statistical rates. In contrast to prior work, our proposed protocols improve the dimension dependence a…
ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems
Qi Pang, Yuanyuan Yuan, Shuai Wang +1
Vertical federated learning (VFL) system has recently become prominent as a concept to process data distributed across many individual sources without the need to centralize it. Mu…