15 citations · 19 across the 13 of their papers we have counts for
14 papers
AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science
An Luo, Jin Du, Xun Xian +12
Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) and artificial…
Can Agentic AI Match the Performance of Human Data Scientists?
An Luo, Jin Du, Fangqiao Tian +9
Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) have significa…
STAC: When Innocent Tools Form Dangerous Chains for LLM Agents
Jing-Jing Li, Jianfeng He, Chao Shang +6
As LLMs advance into autonomous agents with tool-use capabilities, they introduce security challenges that extend beyond traditional content-based LLM safety concerns. This paper i…
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science
An Luo, Xun Xian, Jin Du +12
Large language models (LLMs) have advanced the automation of data science workflows. Yet it remains unclear whether they can critically leverage external domain knowledge as human…
An Outlook on the Opportunities and Challenges of Multi-Agent AI Systems
Fangqiao Tian, An Luo, Jin Du +12
A multi-agent AI system (MAS) is composed of multiple autonomous agents that interact, exchange information, and make decisions based on internal generative models. Recent advances…
Ice Cream Doesn't Cause Drowning: Benchmarking LLMs Against Statistical Pitfalls in Causal Inference
Jin Du, Li Chen, Xun Xian +6
Reliable causal inference is essential for making decisions in high-stakes areas like medicine, economics, and public policy. However, it remains unclear whether large language mod…
Drift to Remember
Jin Du, Xinhe Zhang, Hao Shen +7
Lifelong learning in artificial intelligence (AI) aims to mimic the biological brain's ability to continuously learn and retain knowledge, yet it faces challenges such as catastrop…
On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains
Xun Xian, Ganghua Wang, Xuan Bi +5
Retrieval-Augmented Generation (RAG) has been empirically shown to enhance the performance of large language models (LLMs) in knowledge-intensive domains such as healthcare, financ…
RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees
Xun Xian, Ganghua Wang, Xuan Bi +4
Safeguarding intellectual property and preventing potential misuse of AI-generated images are of paramount importance. This paper introduces a robust and agile plug-and-play waterm…
Demystifying Poisoning Backdoor Attacks from a Statistical Perspective
Ganghua Wang, Xun Xian, Jayanth Srinivasa +4
The growing dependence on machine learning in real-world applications emphasizes the importance of understanding and ensuring its safety. Backdoor attacks pose a significant securi…
A Framework for Understanding Model Extraction Attack and Defense
Xun Xian, Mingyi Hong, Jie Ding
The privacy of machine learning models has become a significant concern in many emerging Machine-Learning-as-a-Service applications, where prediction services based on well-trained…
ASCII: ASsisted Classification with Ignorance Interchange
Jiaying Zhou, Xun Xian, Na Li +1
The rapid development in data collecting devices and computation platforms produces an emerging number of agents, each equipped with a unique data modality over a particular popula…
Imitation Privacy
Xun Xian, Xinran Wang, Mingyi Hong +2
In recent years, there have been many cloud-based machine learning services, where well-trained models are provided to users on a pay-per-query scheme through a prediction API. The…
Assisted Learning: A Framework for Multi-Organization Learning
Xun Xian, Xinran Wang, Jie Ding +1
In an increasing number of AI scenarios, collaborations among different organizations or agents (e.g., human and robots, mobile units) are often essential to accomplish an organiza…