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Jonas Geiping
Jonas Geiping
ELLIS Institute Tübingen & Max Planck Institute for Intelligent Systems
Bestätigte E-Mail-Adresse bei tuebingen.mpg.de - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Inverting gradients-how easy is it to break privacy in federated learning?
J Geiping, H Bauermeister, H Dröge, M Moeller
Advances in neural information processing systems 33, 16937-16947, 2020
13232020
A watermark for large language models
J Kirchenbauer, J Geiping, Y Wen, J Katz, I Miers, T Goldstein
Proceedings of the 40th International Conference on Machine Learning, 17061 …, 2023
5782023
Diffusion art or digital forgery? investigating data replication in diffusion models
G Somepalli, V Singla, M Goldblum, J Geiping, T Goldstein
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
2742023
Baseline defenses for adversarial attacks against aligned language models
N Jain, A Schwarzschild, Y Wen, G Somepalli, J Kirchenbauer, P Chiang, ...
arXiv preprint arXiv:2309.00614, 2023
261*2023
Cold diffusion: Inverting arbitrary image transforms without noise
A Bansal, E Borgnia, HM Chu, J Li, H Kazemi, F Huang, M Goldblum, ...
Advances in Neural Information Processing Systems 36, 2023
2422023
Witches' brew: Industrial scale data poisoning via gradient matching
J Geiping, L Fowl, WR Huang, W Czaja, G Taylor, M Moeller, T Goldstein
Ninth International Conference on Learning Representations 2021, 2021
2302021
Metapoison: Practical general-purpose clean-label data poisoning
WR Huang, J Geiping, L Fowl, G Taylor, T Goldstein
Advances in Neural Information Processing Systems 33, 12080-12091, 2020
2192020
Universal guidance for diffusion models
A Bansal, HM Chu, A Schwarzschild, S Sengupta, M Goldblum, J Geiping, ...
The Twelfth International Conference on Learning Representations, 2024
217*2024
Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery
Y Wen, N Jain, J Kirchenbauer, M Goldblum, J Geiping, T Goldstein
Advances in Neural Information Processing Systems 36, 2023
1992023
On the reliability of watermarks for large language models
J Kirchenbauer, J Geiping, Y Wen, M Shu, K Saifullah, ...
The Twelfth International Conference on Learning Representations, 2023
144*2023
Robbing the fed: Directly obtaining private data in federated learning with modified models
L Fowl, J Geiping, W Czaja, M Goldblum, T Goldstein
Tenth International Conference on Learning Representations, 2022
1412022
Strong data augmentation sanitizes poisoning and backdoor attacks without an accuracy tradeoff
E Borgnia, V Cherepanova, L Fowl, A Ghiasi, J Geiping, M Goldblum, ...
ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021
1382021
Adversarial examples make strong poisons
L Fowl, M Goldblum, P Chiang, J Geiping, W Czaja, T Goldstein
Advances in Neural Information Processing Systems 34, 30339–30351, 2021
1352021
A Cookbook of Self-Supervised Learning
R Balestriero, M Ibrahim, V Sobal, A Morcos, S Shekhar, T Goldstein, ...
arXiv preprint arXiv:2304.12210, 2023
130*2023
Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images
Y Wen, J Kirchenbauer, J Geiping, T Goldstein
Advances in Neural Information Processing Systems 36, 2023
119*2023
Understanding and mitigating copying in diffusion models
G Somepalli, V Singla, M Goldblum, J Geiping, T Goldstein
Advances in Neural Information Processing Systems 36, 2023
972023
Fishing for user data in large-batch federated learning via gradient magnification
Y Wen, J Geiping, L Fowl, M Goldblum, T Goldstein
Proceedings of the 39th International Conference on Machine Learning, 23668 …, 2022
912022
What Doesn't Kill You Makes You Robust (er): Adversarial Training against Poisons and Backdoors
J Geiping, L Fowl, G Somepalli, M Goldblum, M Moeller, T Goldstein
ICLR 2021 Workshop on Security and Safety in Machine Learning Systems, 2021
81*2021
On the exploitability of instruction tuning
M Shu, J Wang, C Zhu, J Geiping, C Xiao, T Goldstein
Advances in Neural Information Processing Systems 36, 2023
782023
Stochastic training is not necessary for generalization
J Geiping, M Goldblum, PE Pope, M Moeller, T Goldstein
The Tenth International Conference on Learning Representations, 2022
772022
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