Pang, Tianyu

Black-box Detection of Backdoor Attacks with Limited Information and Data

Dong, Y., Yang, X., Deng, Z., Pang, T., Xiao, Z., Su, H., & Zhu, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Adversarial Robustness in Machine LearningDigital Media Forensic Detection

Cited by 92

Improved Few-Shot Jailbreaking Can Circumvent Aligned Language Models and Their Defenses

Zheng, X., Pang, T., Du, C., Liu, Q., Jiang, J., & Lin, M. (2024) · Advances in Neural Information Processing Systems 37

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 24

Improving Your Model Ranking on Chatbot Arena by Vote Rigging

Min, R., Pang, T., Du, C., Liu, Q., Cheng, M., & Lin, M. (2025) · arXiv

Adversarial Robustness in Machine LearningSpam and Phishing Detection

Cited by 12

Understanding R1-Zero-Like Training: A Critical Perspective

Liu, Z., Chen, C., Li, W., Qi, P., Pang, T., Du, C., Lee, W. S., & Lin, M. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 8

Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One

Li, T., Zhang, X., Du, C., Pang, T., Liu, Q., Guo, Q., Shen, C., & Liu, Y. (2024) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 3

FlowReasoner: Reinforcing Query-Level Meta-Agents

Gao, H., Liu, Y., He, Y., Dou, L., Du, C., Deng, Z., Hooi, B., Lin, M., & Pang, T. (2025) · arXiv

Software Engineering ResearchAI-based Problem Solving and Planning

Cited by 0

Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates

Zheng, X., Pang, T., Du, C., Liu, Q., Jiang, J., & Lin, M. (2024) · arXiv

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 0