Hayes, Jamie

18 publications

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 Interpreting the Repeated Token Phenomenon in Large Language Models Itay Yona, Ilia Shumailov, Jamie Hayes, Yossi Gandelsman

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 Measuring Memorization in RLHF for Code Completion Jamie Hayes, Ilia Shumailov, William P. Porter, Aneesh Pappu

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

NeurIPS 2025 Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy Bogdan Kulynych, Juan Felipe Gomez, Georgios Kaissis, Jamie Hayes, Borja Balle, Flavio Calmon, Jean Louis Raisaro

NeurIPS 2024 Beyond Slow Signs in High-Fidelity Model Extraction Hanna Foerster, Robert Mullins, Ilia Shumailov, Jamie Hayes

ICML 2024 Beyond the Calibration Point: Mechanism Comparison in Differential Privacy Georgios Kaissis, Stefan Kolek, Borja Balle, Jamie Hayes, Daniel Rueckert

NeurIPSW 2024 Buffer Overflow in Mixture of Experts Jamie Hayes, Ilia Shumailov, Itay Yona

NeurIPS 2023 Bounding Training Data Reconstruction in DP-SGD Jamie Hayes, Borja Balle, Saeed Mahloujifar

UAI 2023 Mnemonist: Locating Model Parameters That Memorize Training Examples Ali Shahin Shamsabadi, Jamie Hayes, Borja Balle, Adrian Weller

NeurIPS 2023 Towards Unbounded Machine Unlearning Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, Eleni Triantafillou

NeurIPSW 2021 Reconstructing Training Data with Informed Adversaries Borja Balle, Giovanni Cherubin, Jamie Hayes

ICLR 2020 A Framework for Robustness Certification of Smoothed Classifiers Using F-Divergences Krishnamurthy Dvijotham, Jamie Hayes, Borja Balle, Zico Kolter, Chongli Qin, Andras Gyorgy, Kai Xiao, Sven Gowal, Pushmeet Kohli

CVPRW 2020 Extensions and Limitations of Randomized Smoothing for Robustness Guarantees Jamie Hayes

NeurIPS 2018 Contamination Attacks and Mitigation in Multi-Party Machine Learning Jamie Hayes, Olga Ohrimenko

CVPRW 2018 On Visible Adversarial Perturbations & Digital Watermarking Jamie Hayes

NeurIPS 2017 Generating Steganographic Images via Adversarial Training Jamie Hayes, George Danezis