| Fast is better than free: Revisiting adversarial training E Wong, L Rice, JZ Kolter arXiv preprint arXiv:2001.03994, 2020 | 1426 | 2020 |
| Overfitting in adversarially robust deep learning L Rice, E Wong, Z Kolter International conference on machine learning, 8093-8104, 2020 | 1003 | 2020 |
| Generating families of practical fast matrix multiplication algorithms J Huang, L Rice, DA Matthews, RA van de Geijn 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2017 | 40 | 2017 |
| (Certified!!) adversarial robustness for free! N Carlini, F Tramer, KD Dvijotham, L Rice, M Sun, JZ Kolter arXiv preprint arXiv:2206.10550, 2022 | 28 | 2022 |
| Robustness between the worst and average case L Rice, A Bair, H Zhang, JZ Kolter Advances in Neural Information Processing Systems 34, 27840-27851, 2021 | 22 | 2021 |
| Certified robustness against adversarial patch attacks via randomized cropping WY Lin, F Sheikholeslami, L Rice, JZ Kolter ICML 2021 Workshop on Adversarial Machine Learning, 2021 | 9 | 2021 |
| Certified robustness against physically-realizable patch attack via randomized cropping WY Lin, F Sheikholeslami, L Rice, JZ Kolter | 6 | 2021 |
| Empirical robustification of pre-trained classifiers MS Norouzzadeh, WY Lin, L Boytsov, L Rice, H Zhang, F Condessa, ... ICML 2021 Workshop on Adversarial Machine Learning, 2021 | 1 | 2021 |
| Methods for robust training and evaluation of deep neural networks L Rice Carnegie Mellon University, 2023 | | 2023 |
| Performance Optimization for the K-Nearest Neighbors Kernel using Strassen’s Algorithm L Rice, R van de Geijn, D Fussell, V Ramachandran | | 2017 |
