29 citations · 75 across the 35 of their papers we have counts for
8 papers · 1 filter
Modeling and Designing Non-Pharmaceutical Interventions in Epidemics: A Submodular Approach
Shiyu Cheng, Luyao Niu, Bhaskar Ramasubramanian +2
This paper considers the problem of designing non-pharmaceutical intervention (NPI) strategies, such as masking and social distancing, to slow the spread of a viral epidemic. We fo…
Who is Responsible? Explaining Safety Violations in Multi-Agent Cyber-Physical Systems
Luyao Niu, Hongchao Zhang, Dinuka Sahabandu +3
Multi-agent cyber-physical systems are present in a variety of applications. Agent decision-making can be affected due to errors induced by uncertain, dynamic operating environment…
A Method for Fast Autonomy Transfer in Reinforcement Learning
Dinuka Sahabandu, Bhaskar Ramasubramanian, Michail Alexiou +3
This paper introduces a novel reinforcement learning (RL) strategy designed to facilitate rapid autonomy transfer by utilizing pre-trained critic value functions from multiple envi…
CleanGen: Mitigating Backdoor Attacks for Generation Tasks in Large Language Models
Yuetai Li, Zhangchen Xu, Fengqing Jiang +4
The remarkable performance of large language models (LLMs) in generation tasks has enabled practitioners to leverage publicly available models to power custom applications, such as…
Game of Trojans: Adaptive Adversaries Against Output-based Trojaned-Model Detectors
Dinuka Sahabandu, Xiaojun Xu, Arezoo Rajabi +4
We propose and analyze an adaptive adversary that can retrain a Trojaned DNN and is also aware of SOTA output-based Trojaned model detectors. We show that such an adversary can ens…
Double-Dip: Thwarting Label-Only Membership Inference Attacks with Transfer Learning and Randomization
Arezoo Rajabi, Reeya Pimple, Aiswarya Janardhanan +3
Transfer learning (TL) has been demonstrated to improve DNN model performance when faced with a scarcity of training samples. However, the suitability of TL as a solution to reduce…
ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs
Fengqing Jiang, Zhangchen Xu, Luyao Niu +4
Safety is critical to the usage of large language models (LLMs). Multiple techniques such as data filtering and supervised fine-tuning have been developed to strengthen LLM safety.…
BadChain: Backdoor Chain-of-Thought Prompting for Large Language Models
Zhen Xiang, Fengqing Jiang, Zidi Xiong +3
Large language models (LLMs) are shown to benefit from chain-of-thought (COT) prompting, particularly when tackling tasks that require systematic reasoning processes. On the other…