Li, Bo

Trustworthy AI: From Principles to Practices

Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys

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Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses

Goldblum, M., Tsipras, D., Xie, C., Chen, X., Schwarzschild, A., Song, D., Madry, A., Li, B., & Goldstein, T. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

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Reinforcement Learning with Perturbed Rewards

Wang, J., Liu, Y., & Li, B. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

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Stability-Based Analysis and Defense against Backdoor Attacks on Edge Computing Services

Zhao, Y., Xu, K., Wang, H., Li, B., & Jia, R. (2021) · IEEE Network

Adversarial Robustness in Machine LearningIoT and Edge/Fog Computing

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Introducing v0.5 of the AI Safety Benchmark from MLCommons

Vidgen, B., Agrawal, A., Ahmed, A. M., Akinwande, V., Al-Nuaimi, N., Alfaraj, N., Alhajjar, E., Aroyo, L., Bavalatti, T., Bartolo, M., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

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