3 citations · 4 across the 12 of their papers we have counts for
12 papers
CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning
Zijian Jiang, Chaoli Sun, Handing Wang +1
One-shot federated learning (OSFL) has emerged as a promising collaborative model learning framework with only a single round of communication, offering significant advantages in c…
Overlooked Safety Vulnerability in LLMs: Malicious Intelligent Optimization Algorithm Request and its Jailbreak
Haoran Gu, Handing Wang, Yi Mei +2
The widespread deployment of large language models (LLMs) has raised growing concerns about their misuse risks and associated safety issues. While prior studies have examined the s…
When Alignment Fails: Multimodal Adversarial Attacks on Vision-Language-Action Models
Yuping Yan, Yuhan Xie, Yixin Zhang +3
Vision-Language-Action models (VLAs) have recently demonstrated remarkable progress in embodied environments, enabling robots to perceive, reason, and act through unified multimoda…
Solver-Independent Automated Problem Formulation via LLMs for High-Cost Simulation-Driven Design
Yuchen Li, Handing Wang, Bing Xue +2
In the high-cost simulation-driven design domain, translating ambiguous design requirements into a mathematical optimization formulation is a bottleneck for optimizing product perf…
Enhancing the Effectiveness and Durability of Backdoor Attacks in Federated Learning through Maximizing Task Distinction
Zhaoxin Wang, Handing Wang, Cong Tian +1
Federated learning allows multiple participants to collaboratively train a central model without sharing their private data. However, this distributed nature also exposes new attac…
Decoding One Safety Trigger Token for Balancing Safety and Usability in Large Language Models
Haoran Gu, Handing Wang, Yi Mei +2
Large Language Models (LLMs) have been extensively used across diverse domains, including virtual assistants, automated code generation, and scientific research. However, they rema…
ParetoHqD: Fast Offline Multiobjective Alignment of Large Language Models using Pareto High-quality Data
Haoran Gu, Handing Wang, Yi Mei +2
Aligning large language models with multiple human expectations and values is crucial for ensuring that they adequately serve a variety of user needs. To this end, offline multiobj…
Token-Level Constraint Boundary Search for Jailbreaking Text-to-Image Models
Jiangtao Liu, Zhaoxin Wang, Handing Wang +2
Text-to-Image (T2I) generation has advanced rapidly in recent years, but they also raise safety concerns due to the potential production of harmful content. In the practical deploy…
Preventing Catastrophic Overfitting in Fast Adversarial Training: A Bi-level Optimization Perspective
Zhaoxin Wang, Handing Wang, Cong Tian +1
Adversarial training (AT) has become an effective defense method against adversarial examples (AEs) and it is typically framed as a bi-level optimization problem. Among various AT…
Rapid Plug-in Defenders
Kai Wu, Yujian Betterest Li, Jian Lou +3
In the realm of daily services, the deployment of deep neural networks underscores the paramount importance of their reliability. However, the vulnerability of these networks to ad…
B2Opt: Learning to Optimize Black-box Optimization with Little Budget
Xiaobin Li, Kai Wu, Xiaoyu Zhang +2
The core challenge of high-dimensional and expensive black-box optimization (BBO) is how to obtain better performance faster with little function evaluation cost. The essence of th…
Higher-order Knowledge Transfer for Dynamic Community Detection with Great Changes
Huixin Ma, Kai Wu, Handing Wang +1
Network structure evolves with time in the real world, and the discovery of changing communities in dynamic networks is an important research topic that poses challenging tasks. Mo…