| A watermark for large language models J Kirchenbauer, J Geiping, Y Wen, J Katz, I Miers, T Goldstein arXiv preprint arXiv:2301.10226, 2023 | 162 | 2023 |
| 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 arXiv preprint arXiv:2302.03668, 2023 | 41 | 2023 |
| Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust Y Wen, J Kirchenbauer, J Geiping, T Goldstein arXiv preprint arXiv:2305.20030, 2023 | 12 | 2023 |
| A closer look at distribution shifts and out-of-distribution generalization on graphs M Ding, K Kong, J Chen, J Kirchenbauer, M Goldblum, D Wipf, F Huang, ... | 11 | 2021 |
| Bring Your Own Data! Self-Supervised Evaluation for Large Language Models N Jain, K Saifullah, Y Wen, J Kirchenbauer, M Shu, A Saha, M Goldblum, ... arXiv preprint arXiv:2306.13651, 2023 | 9 | 2023 |
| On the Reliability of Watermarks for Large Language Models J Kirchenbauer, J Geiping, Y Wen, M Shu, K Saifullah, K Kong, ... arXiv preprint arXiv:2306.04634, 2023 | 8 | 2023 |
| Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, and Tom Goldstein. 2023b J Kirchenbauer, J Geiping, Y Wen, M Shu, K Saifullah On the reliability of watermarks for large language models, 0 | 8 | |
| A watermark for large language models. ArXiv J Kirchenbauer, J Geiping, Y Wen, J Katz, I Miers, T Goldstein arXiv preprint arXiv:2301.10226, 2023 | 5 | 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 | 2 | 2023 |
| GOAT: A global transformer on large-scale graphs K Kong, J Chen, J Kirchenbauer, R Ni, CB Bruss, T Goldstein International Conference on Machine Learning, 17375-17390, 2023 | 1 | 2023 |
| What is Your Metric Telling You? Evaluating Classifier Calibration under Context-Specific Definitions of Reliability J Kirchenbauer, J Oaks, E Heim | 1 | 2022 |
| Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images Y Wen, J Kirchenbauer, J Geiping, T Goldstein Thirty-seventh Conference on Neural Information Processing Systems, 2023 | | 2023 |
| NEFTune: Noisy Embeddings Improve Instruction Finetuning N Jain, P Chiang, Y Wen, J Kirchenbauer, HM Chu, G Somepalli, ... arXiv preprint arXiv:2310.05914, 2023 | | 2023 |
| 17084 J Kirchenbauer, J Geiping, Y Wen, J Katz, I Miers, T Goldstein Proceedings of the 40th International Conference on Machine Learning, 17061 …, 2023 | | 2023 |
| Knowing When You Don't Know: Quantifying and Reasoning about Uncertainty in Machine Learning Models E Heim, J Kirchenbauer, J Helland, J Oaks, A Singh, Z Lipton | | 2022 |
| How to Do a Vocab Swap? A Study of Embedding Replacement for Pre-trained Transformers N Jain, J Kirchenbauer, J Geiping, T Goldstein | | 2022 |
| In Search of Out-of-Distribution Generalization on Graphs M Ding, K Kong, J Chen, J Kirchenbauer, M Goldblum, D Wipf, F Huang, ... | | |
Tom GoldsteinVolpi-Cupal Professor of Computer Science, University of MarylandVerified email at cs.umd.edu
