Jonas Geiping

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Tom GoldsteinVolpi-Cupal Professor of Computer Science, University of MarylandDirección de correo verificada de cs.umd.edu
Micah GoldblumColumbia UniversityDirección de correo verificada de columbia.edu
Yuxin WenUniversity of MarylandDirección de correo verificada de umd.edu
John KirchenbauerUniversity of Maryland, College ParkDirección de correo verificada de umd.edu
Michael MoellerProfessor for Computer Vision, University of Siegen, GermanyDirección de correo verificada de uni-siegen.de
Liam FowlGoogleDirección de correo verificada de google.com
Gowthami SomepalliUniversity of Maryland, College ParkDirección de correo verificada de umd.edu
Neel JainUniversity of Maryland, College ParkDirección de correo verificada de umd.edu
Arpit BansalUniversity of Maryland, College ParkDirección de correo verificada de umd.edu
Vasu SinglaUniversity of MarylandDirección de correo verificada de cs.umd.edu
Hong-Min ChuDepartment of Computer Science, University of MarylandDirección de correo verificada de cs.umd.edu
Avi SchwarzschildPost Doc at Carnegie Mellon UniversityDirección de correo verificada de cmu.edu
Ping-yeh ChiangUniversity of Maryland - College ParkDirección de correo verificada de cs.umd.edu
Manli ShuSalesforce AI ResearchDirección de correo verificada de umd.edu
Furong HuangAssociate Professor of Computer Science, University of MarylandDirección de correo verificada de umd.edu
Eitan BorgniaPhD Student UChicagoDirección de correo verificada de uchicago.edu
Ian MiersUniversity of MarylandDirección de correo verificada de umd.edu
Hamid KazemiApple Inc.Dirección de correo verificada de apple.com
Aniruddha SahaDirección de correo verificada de umbc.edu
Khalid SaifullahPhD Student of Computer Science, University of MarylandDirección de correo verificada de cs.umd.edu

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Jonas Geiping

ELLIS Institute Tübingen & Max Planck Institute for Intelligent Systems

Dirección de correo verificada de tuebingen.mpg.de - Página principal

Machine Learning ML Safety Security Privacy Optimization


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	1414	2020
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	649	2023
A Cookbook of Self-Supervised Learning R Balestriero, M Ibrahim, V Sobal, A Morcos, S Shekhar, T Goldstein, ... arXiv preprint arXiv:2304.12210, 2023	487*	2023
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	312*	2023
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	293	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	264	2023
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	255*	2024
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	244	2021
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	231	2023
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	226	2020
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	162*	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	154	2022
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	144	2021
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	139	2021
Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images Y Wen, J Kirchenbauer, J Geiping, T Goldstein Advances in Neural Information Processing Systems 36, 2023	138*	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	107	2023
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	94	2022
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	87	2023
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	86*	2021
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	81	2022

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