| 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 | 1317 | 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 | 570 | 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 | 269 | 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 | 257* | 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 | 240 | 2023 |
| 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 | 229 | 2021 |
| 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 | 218 | 2020 |
| 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 | 211* | 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 | 198 | 2023 |
| 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 | 143* | 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 | 141 | 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 | 138 | 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 | 133 | 2021 |
| A Cookbook of Self-Supervised Learning R Balestriero, M Ibrahim, V Sobal, A Morcos, S Shekhar, T Goldstein, ... arXiv preprint arXiv:2304.12210, 2023 | 127* | 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 | 118* | 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 | 96 | 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 | 91 | 2022 |
| 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 |
| 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 | 77 | 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 | 76 | 2023 |
Tom GoldsteinVolpi-Cupal Professor of Computer Science, University of MarylandVerifierad e-postadress på cs.umd.edu
