| Certified defenses for adversarial patches P Chiang, R Ni, A Abdelkader, C Zhu, C Studor, T Goldstein 8th International Conference on Learning Representations (ICLR 2020), 2020 | 172 | 2020 |
| Adversarial examples make strong poisons L Fowl, M Goldblum, P Chiang, J Geiping, W Czaja, T Goldstein Advances in Neural Information Processing Systems 34 (NeurIPS 2021), 2021 | 111 | 2021 |
| Compressing gans using knowledge distillation A Aguinaldo, PY Chiang, A Gain, A Patil, K Pearson, S Feizi arXiv preprint arXiv:1902.00159, 2019 | 96 | 2019 |
| Detection as Regression: Certified Object Detection by Median Smoothing P Chiang, MJ Curry, A Abdelkader, A Kumar, J Dickerson, T Goldstein Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 66 | 2020 |
| Can neural nets learn the same model twice? investigating reproducibility and double descent from the decision boundary perspective G Somepalli, L Fowl, A Bansal, P Yeh-Chiang, Y Dar, R Baraniuk, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 63 | 2022 |
| 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 | 43 | 2023 |
| Preventing unauthorized use of proprietary data: Poisoning for secure dataset release L Fowl, P Chiang, M Goldblum, J Geiping, A Bansal, W Czaja, T Goldstein arXiv preprint arXiv:2103.02683, 2021 | 40 | 2021 |
| Certified neural network watermarks with randomized smoothing A Bansal, P Chiang, MJ Curry, R Jain, C Wigington, V Manjunatha, ... International Conference on Machine Learning, 1450-1465, 2022 | 39 | 2022 |
| Certifying Strategyproof Auction Networks MJ Curry, PY Chiang, T Goldstein, J Dickerson Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 39 | 2020 |
| Active Learning at the ImageNet Scale ZA Sami Emam, HM Chu, PY Chiang, W Czaja, R Leapman, M Goldblum, ... arXiv e-prints, arXiv: 2111.12880, 2021 | 33* | 2021 |
| Proportionnet: Balancing fairness and revenue for auction design with deep learning K Kuo, A Ostuni, E Horishny, MJ Curry, S Dooley, P Chiang, T Goldstein, ... arXiv preprint arXiv:2010.06398, 2020 | 33 | 2020 |
| Neftune: Noisy embeddings improve instruction finetuning N Jain, P Chiang, Y Wen, J Kirchenbauer, HM Chu, G Somepalli, ... arXiv preprint arXiv:2310.05914, 2023 | 31 | 2023 |
| Wrapnet: Neural net inference with ultra-low-precision arithmetic R Ni, H Chu, O Castañeda Fernández, P Chiang, C Studer, T Goldstein International Conference on Learning Representations ICLR 2021, 2021 | 28* | 2021 |
| Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent P Chiang, R Ni, DY Miller, A Bansal, J Geiping, M Goldblum, T Goldstein International Conference on Learning Representations, 2023 | 17 | 2023 |
| Witchcraft: Efficient PGD attacks with random step size PY Chiang, J Geiping, M Goldblum, T Goldstein, R Ni, S Reich, A Shafahi ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 16 | 2020 |
| Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, and Tom Goldstein. 2023 N Jain, P yeh Chiang, Y Wen, J Kirchenbauer, HM Chu, G Somepalli, ... Neftune: Noisy embeddings improve instruction finetuning. CoRR, abs/2310.05914, 0 | 9 | |
| K-sam: Sharpness-aware minimization at the speed of sgd R Ni, P Chiang, J Geiping, M Goldblum, AG Wilson, T Goldstein arXiv preprint arXiv:2210.12864, 2022 | 5 | 2022 |
| Improving the tightness of convex relaxation bounds for training certifiably robust classifiers C Zhu, R Ni, P Chiang, H Li, F Huang, T Goldstein arXiv preprint arXiv:2002.09766, 2020 | 4 | 2020 |
| Protecting proprietary data: Poisoning for secure dataset release LH Fowl, P Chiang, M Goldblum, J Geiping, AA Bansal, W Czaja, ... | 1 | 2021 |
| Universal Pyramid Adversarial Training for Improved ViT Performance P Chiang, Y Zhou, O Poursaeed, SN Shukla, A Shah, T Goldstein, SN Lim arXiv preprint arXiv:2312.16339, 2023 | | 2023 |
Tom GoldsteinVolpi-Cupal Professor of Computer Science, University of Maryland在 cs.umd.edu 的电子邮件经过验证
