Jagielski, Matthew

Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

Jagielski, M., Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., & Li, B. (2018) · 2018 IEEE Symposium on Security and Privacy (SP)

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 861

PaLM 2 Technical Report

Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 834

Extracting Training Data from Large Language Models

Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al. (2020) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 727

Quantifying Memorization Across Neural Language Models

Carlini, N., Ippolito, D., Jagielski, M., Lee, K., Tramer, F., & Zhang, C. (2022) · arXiv

Topic ModelingPrivacy-Preserving Technologies in Data

Cited by 204

Poisoning Web-Scale Training Datasets is Practical

Carlini, N., Jagielski, M., Choquette-Choo, C. A., Paleka, D., Pearce, W., Anderson, H., Terzis, A., Thomas, K., & Tramèr, F. (2024) · 2024 IEEE Symposium on Security and Privacy (SP)

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 138

Poisoning Web-Scale Training Datasets is Practical

Carlini, N., Jagielski, M., Choquette-Choo, C. A., Paleka, D., Pearce, W., Anderson, H., Terzis, A., Thomas, K., & Tramèr, F. (2023) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 138

Scalable Extraction of Training Data from (Production) Language Models

Nasr, M., Carlini, N., Hayase, J., Jagielski, M., Cooper, A. F., Ippolito, D., Choquette-Choo, C. A., Wallace, E., Tramèr, F., & Lee, K. (2023) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 96

Subpopulation Data Poisoning Attacks

Jagielski, M., Severi, G., Pousette Harger, N., & Oprea, A. (2021) · Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 86

Differentially Private Fair Learning

Jagielski, M., Kearns, M., Mao, J., Oprea, A., Roth, A., Sharifi-Malvajerdi, S., & Ullman, J. (2018) · arXiv

Privacy-Preserving Technologies in DataEthics and Social Impacts of AI

Cited by 62

Counterfactual Memorization in Neural Language Models

Zhang, C., Ippolito, D., Lee, K., Jagielski, M., Tramèr, F., & Carlini, N. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 54

Preventing generation of verbatim memorization in language models gives a false sense of privacy

Ippolito, D., Tramer, F., Nasr, M., Zhang, C., Jagielski, M., Lee, K., Choquette Choo, C., & Carlini, N. (2023) · Proceedings of the 16th International Natural Language Generation Conference

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 28

Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards

Huang, Y., Nasr, M., Angelopoulos, A., Carlini, N., Chiang, W.-L., Choquette-Choo, C. A., Ippolito, D., Jagielski, M., Lee, K., Liu, K. Z., et al. (2025) · arXiv

Adversarial Robustness in Machine LearningSpam and Phishing Detection

Cited by 17

Privacy Side Channels in Machine Learning Systems

Debenedetti, E., Severi, G., Carlini, N., Choquette-Choo, C. A., Jagielski, M., Nasr, M., Wallace, E., & Tramèr, F. (2023) · arXiv

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 6