Christopher A. Choquette-Choo
Christopher A. Choquette-Choo
Other namesChristopher Choquette-Choo, Christopher Choquette
Google DeepMind
Verified email at - Homepage
Cited by
Cited by
Palm 2 technical report
R Anil, AM Dai, O Firat, M Johnson, D Lepikhin, A Passos, S Shakeri, ...
arXiv preprint arXiv:2305.10403, 2023
Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ...
arXiv preprint arXiv:2312.11805, 2023
Machine unlearning
L Bourtoule, V Chandrasekaran, CA Choquette-Choo, H Jia, A Travers, ...
42nd IEEE Symposium on Security and Privacy, 2021
Label-Only Membership Inference Attacks
CA Choquette-Choo, F Tramer, N Carlini, N Papernot
38th International Conference on Machine Learning, 2021
Gemma: Open models based on gemini research and technology
G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ...
arXiv preprint arXiv:2403.08295, 2024
Entangled watermarks as a defense against model extraction
H Jia, CA Choquette-Choo, V Chandrasekaran, N Papernot
30th USENIX security symposium (USENIX Security 21), 1937-1954, 2021
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ...
arXiv preprint arXiv:2403.05530, 2024
Are aligned neural networks adversarially aligned?
N Carlini, M Nasr, CA Choquette-Choo, M Jagielski, I Gao, PWW Koh, ...
Advances in Neural Information Processing Systems 36, 2024
Scalable extraction of training data from (production) language models
M Nasr, N Carlini, J Hayase, M Jagielski, AF Cooper, D Ippolito, ...
arXiv preprint arXiv:2311.17035, 2023
Poisoning web-scale training datasets is practical
N Carlini, M Jagielski, CA Choquette-Choo, D Paleka, W Pearce, ...
arXiv preprint arXiv:2302.10149, 2023
Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy
D Ippolito, F Tramèr, M Nasr, C Zhang, M Jagielski, K Lee, CC Choo, ...
Proceedings of the 16th International Natural Language Generation Conference …, 2023
Proof-of-Learning: Definitions and Practice
H Jia, M Yaghini, CA Choquette-Choo, N Dullerud, A Thudi, ...
42nd IEEE Symposium on Security and Privacy, 2021
CaPC Learning: Confidential and Private Collaborative Learning
CA Choquette-Choo, N Dullerud, A Dziedzic, Y Zhang, S Jha, N Papernot, ...
9th International Conference on Learning Representations, 2021
Federated learning of gboard language models with differential privacy
Z Xu, Y Zhang, G Andrew, CA Choquette-Choo, P Kairouz, HB McMahan, ...
arXiv preprint arXiv:2305.18465, 2023
The fundamental price of secure aggregation in differentially private federated learning
WN Chen, CAC Choo, P Kairouz, AT Suresh
International Conference on Machine Learning, 3056-3089, 2022
Madlad-400: A multilingual and document-level large audited dataset
S Kudugunta, I Caswell, B Zhang, X Garcia, CA Choquette-Choo, K Lee, ...
Advances in Neural Information Processing Systems 36, 2024
Multi-epoch matrix factorization mechanisms for private machine learning
CA Choquette-Choo, HB McMahan, K Rush, A Thakurta
40th International Conference on Machine Learning 202, 5924-5963, 2023
Privacy side channels in machine learning systems
E Debenedetti, G Severi, N Carlini, CA Choquette-Choo, M Jagielski, ...
arXiv preprint arXiv:2309.05610, 2023
A Multi-label, Dual-Output Deep Neural Network for Automated Bug Triaging
CA Choquette-Choo, D Sheldon, J Proppe, J Alphonso-Gibbs, H Gupta
2019 18th IEEE International Conference On Machine Learning And Applications …, 2019
(Amplified) Banded Matrix Factorization: A unified approach to private training
CA Choquette-Choo, A Ganesh, R McKenna, HB McMahan, J Rush, ...
Advances in Neural Information Processing Systems 36, 2024
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