6 citations · 34 across the 24 of their papers we have counts for
8 papers · 1 filter
Token-Level Adversarial Prompt Detection Based on Perplexity Measures and Contextual Information
Zhengmian Hu, Gang Wu, Saayan Mitra +4
In recent years, Large Language Models (LLM) have emerged as pivotal tools in various applications. However, these models are susceptible to adversarial prompt attacks, where attac…
GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Dataset
Ruibo Chen, Tianyi Xiong, Yihan Wu +6
This technical report delves into the application of GPT-4 Vision (GPT-4V) in the nuanced realm of COVID-19 image classification, leveraging the transformative potential of in-cont…
Unbiased Watermark for Large Language Models
Zhengmian Hu, Lichang Chen, Xidong Wu +3
The recent advancements in large language models (LLMs) have sparked a growing apprehension regarding the potential misuse. One approach to mitigating this risk is to incorporate w…
Solving a Class of Non-Convex Minimax Optimization in Federated Learning
Xidong Wu, Jianhui Sun, Zhengmian Hu +2
The minimax problems arise throughout machine learning applications, ranging from adversarial training and policy evaluation in reinforcement learning to AUROC maximization. To add…
Federated Conditional Stochastic Optimization
Xidong Wu, Jianhui Sun, Zhengmian Hu +3
Conditional stochastic optimization has found applications in a wide range of machine learning tasks, such as invariant learning, AUPRC maximization, and meta-learning. As the dema…
A Resilient and Accessible Distribution-Preserving Watermark for Large Language Models
Yihan Wu, Zhengmian Hu, Junfeng Guo +2
Watermarking techniques offer a promising way to identify machine-generated content via embedding covert information into the contents generated from language models. A challenge i…
Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining
Xidong Wu, Zhengmian Hu, Jian Pei +1
Multi-party collaborative training, such as distributed learning and federated learning, is used to address the big data challenges. However, traditional multi-party collaborative…
Decentralized Riemannian Algorithm for Nonconvex Minimax Problems
Xidong Wu, Zhengmian Hu, Heng Huang
The minimax optimization over Riemannian manifolds (possibly nonconvex constraints) has been actively applied to solve many problems, such as robust dimensionality reduction and de…