Assisted Learning: A Framework for Multi-Organization Learning. - Xun Xian; Xinran Wang; Jie Ding 002; Reza Ghanadan (NeurIPS, 2020)

Assisted Learning and Imitation Privacy. - Xun Xian; Xinran Wang; Jie Ding 002; Reza Ghanadan (CoRR, 2020)

Imitation Privacy. - Xun Xian; Xinran Wang; Mingyi Hong 001; Jie Ding 002; Reza Ghanadan (CoRR, 2020)

ASCII: ASsisted Classification with Ignorance Interchange. - Jiaying Zhou; Xun Xian; Na Li 002; Jie Ding 002 (CoRR, 2020)

Assisted Learning: Cooperative AI with Autonomy. - Jiaying Zhou; Xun Xian; Na Li 002; Jie Ding 002 (ICASSP, 2021)

Mismatched Supervised Learning. - Xun Xian; Mingyi Hong 001; Jie Ding 002 (ICASSP, 2022)

Understanding Model Extraction Games. - Xun Xian; Mingyi Hong 001; Jie Ding 002 (TPS-ISA, 2022)

A Framework for Understanding Model Extraction Attack and Defense. - Xun Xian; Mingyi Hong 001; Jie Ding 002 (CoRR, 2022)

Understanding Backdoor Attacks through the Adaptability Hypothesis. - Xun Xian; Ganghua Wang; Jayanth Srinivasa; Ashish Kundu; Xuan Bi; Mingyi Hong 001; Jie Ding 002 (ICML, 2023)

A Unified Detection Framework for Inference-Stage Backdoor Defenses. - Xun Xian; Ganghua Wang; Jayanth Srinivasa; Ashish Kundu; Xuan Bi; Mingyi Hong 001; Jie Ding 002 (NeurIPS, 2023)

Demystifying Poisoning Backdoor Attacks from a Statistical Perspective. - Ganghua Wang; Xun Xian; Ashish Kundu; Jayanth Srinivasa; Xuan Bi; Mingyi Hong 001; Jie Ding 002 (ICLR, 2024)

RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees. - Xun Xian; Ganghua Wang; Xuan Bi; Jayanth Srinivasa; Ashish Kundu; Mingyi Hong 001; Jie Ding 002 (NeurIPS, 2024)

Drift to Remember. - Jin Du; Xinhe Zhang; Hao Shen; Xun Xian; Ganghua Wang; Jiawei Zhang 007; Yuhong Yang 002; Na Li 002; Jia Liu 002; Jie Ding 002 (CoRR, 2024)

On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains. - Xun Xian; Ganghua Wang; Xuan Bi; Jayanth Srinivasa; Ashish Kundu; Charles Fleming; Mingyi Hong 001; Jie Ding 002 (CoRR, 2024)

Ice Cream Doesn't Cause Drowning: Benchmarking LLMs Against Statistical Pitfalls in Causal Inference. - Jin Du; Li Chen; Xun Xian; An Luo; Fangqiao Tian; Ganghua Wang; Charles Doss; Xiaotong Shen; Jie Ding 002 (CoRR, 2025)

An Outlook on the Opportunities and Challenges of Multi-Agent AI Systems. - Fangqiao Tian; An Luo; Jin Du; Xun Xian; Robert Specht; Ganghua Wang; Xuan Bi; Jiawei Zhou; Jayanth Srinivasa; Ashish Kundu; Charles Fleming; Rui Zhang 028; et al. (CoRR, 2025)

AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science. - An Luo; Xun Xian; Jin Du; Fangqiao Tian; Ganghua Wang; Ming Zhong; Shengchun Zhao; Xuan Bi; Zirui Liu 001; Jiawei Zhou; Jayanth Srinivasa; Ashish Kundu; et al. (CoRR, 2025)