Jagielski, Matthew

21 publications

AAAI 2025 Differentially Private Prototypes for Imbalanced Transfer Learning Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch

ICML 2025 Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards Yangsibo Huang, Milad Nasr, Anastasios Nikolas Angelopoulos, Nicholas Carlini, Wei-Lin Chiang, Christopher A. Choquette-Choo, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Ken Liu, Ion Stoica, Florian Tramèr, Chiyuan Zhang

NeurIPS 2025 Exploring the Limits of Strong Membership Inference Attacks on Large Language Models Jamie Hayes, Ilia Shumailov, Christopher A. Choquette-Choo, Matthew Jagielski, Georgios Kaissis, Milad Nasr, Meenatchi Sundaram Muthu Selva Annamalai, Niloofar Mireshghallah, Igor Shilov, Matthieu Meeus, Yves-Alexandre de Montjoye, Katherine Lee, Franziska Boenisch, Adam Dziedzic, A. Feder Cooper

ICML 2025 Language Models May Verbatim Complete Text They Were Not Explicitly Trained on Ken Liu, Christopher A. Choquette-Choo, Matthew Jagielski, Peter Kairouz, Sanmi Koyejo, Percy Liang, Nicolas Papernot

NeurIPS 2025 Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Kevin Klyman, Matthew Jagielski, Katja Filippova, Ken Liu, Alexandra Chouldechova, Jamie Hayes, Yangsibo Huang, Eleni Triantafillou, Peter Kairouz, Nicole Elyse Mitchell, Niloofar Mireshghallah, Abigail Z. Jacobs, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Ilia Shumailov, Andreas Terzis, Solon Barocas, Jennifer Wortman Vaughan, Danah Boyd, Yejin Choi, Sanmi Koyejo, Fernando Delgado, Percy Liang, Daniel E. Ho, Pamela Samuelson, Miles Brundage, David Bau, Seth Neel, Hanna Wallach, Amy B. Cyphert, Mark Lemley, Nicolas Papernot, Katherine Lee

ICLR 2025 On Evaluating the Durability of Safeguards for Open-Weight LLMs Xiangyu Qi, Boyi Wei, Nicholas Carlini, Yangsibo Huang, Tinghao Xie, Luxi He, Matthew Jagielski, Milad Nasr, Prateek Mittal, Peter Henderson

ICLR 2025 Scalable Extraction of Training Data from Aligned, Production Language Models Milad Nasr, Javier Rando, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Florian Tramèr, Katherine Lee

ICLR 2025 The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD Milad Nasr, Thomas Steinke, Borja Balle, Christopher A. Choquette-Choo, Arun Ganesh, Matthew Jagielski, Jamie Hayes, Abhradeep Guha Thakurta, Adam Smith, Andreas Terzis

ICML 2024 Auditing Private Prediction Karan Chadha, Matthew Jagielski, Nicolas Papernot, Christopher A. Choquette-Choo, Milad Nasr

ICML 2024 Stealing Part of a Production Language Model Nicholas Carlini, Daniel Paleka, Krishnamurthy Dj Dvijotham, Thomas Steinke, Jonathan Hayase, A. Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, Florian Tramèr

NeurIPS 2023 Are Aligned Neural Networks Adversarially Aligned? Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Pang Wei W Koh, Daphne Ippolito, Florian Tramer, Ludwig Schmidt

ICMLW 2023 Backdoor Attacks for In-Context Learning with Language Models Nikhil Kandpal, Matthew Jagielski, Florian Tramèr, Nicholas Carlini

NeurIPS 2023 Counterfactual Memorization in Neural Language Models Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramer, Nicholas Carlini

ICLR 2023 Measuring Forgetting of Memorized Training Examples Matthew Jagielski, Om Thakkar, Florian Tramer, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang

NeurIPS 2023 Privacy Auditing with One (1) Training Run Thomas Steinke, Milad Nasr, Matthew Jagielski

ICMLW 2023 Privacy Auditing with One (1) Training Run Thomas Steinke, Milad Nasr, Matthew Jagielski

ICLR 2023 Quantifying Memorization Across Neural Language Models Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, Chiyuan Zhang

NeurIPS 2023 Students Parrot Their Teachers: Membership Inference on Model Distillation Matthew Jagielski, Milad Nasr, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini, Florian Tramer

NeurIPS 2022 The Privacy Onion Effect: Memorization Is Relative Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramer

NeurIPS 2020 Auditing Differentially Private Machine Learning: How Private Is Private SGD? Matthew Jagielski, Jonathan Ullman, Alina Oprea

ICML 2019 Differentially Private Fair Learning Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman