| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 1056 | 2021 |
| Certified defenses against adversarial examples A Raghunathan, J Steinhardt, P Liang arXiv preprint arXiv:1801.09344, 2018 | 897 | 2018 |
| Unlabeled data improves adversarial robustness Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang Advances in neural information processing systems 32, 2019 | 557 | 2019 |
| Semidefinite relaxations for certifying robustness to adversarial examples A Raghunathan, J Steinhardt, PS Liang Advances in neural information processing systems 31, 2018 | 383 | 2018 |
| Certified robustness to adversarial word substitutions R Jia, A Raghunathan, K Göksel, P Liang arXiv preprint arXiv:1909.00986, 2019 | 222 | 2019 |
| An investigation of why overparameterization exacerbates spurious correlations S Sagawa, A Raghunathan, PW Koh, P Liang International Conference on Machine Learning, 8346-8356, 2020 | 211 | 2020 |
| Just train twice: Improving group robustness without training group information EZ Liu, B Haghgoo, AS Chen, A Raghunathan, PW Koh, S Sagawa, ... International Conference on Machine Learning, 6781-6792, 2021 | 201 | 2021 |
| Adversarial training can hurt generalization A Raghunathan, SM Xie, F Yang, JC Duchi, P Liang arXiv preprint arXiv:1906.06032, 2019 | 201 | 2019 |
| The pitfalls of simplicity bias in neural networks H Shah, K Tamuly, A Raghunathan, P Jain, P Netrapalli Advances in Neural Information Processing Systems 33, 9573-9585, 2020 | 188 | 2020 |
| Fine-tuning can distort pretrained features and underperform out-of-distribution A Kumar, A Raghunathan, R Jones, T Ma, P Liang arXiv preprint arXiv:2202.10054, 2022 | 161 | 2022 |
| Understanding and mitigating the tradeoff between robustness and accuracy A Raghunathan, SM Xie, F Yang, J Duchi, P Liang arXiv preprint arXiv:2002.10716, 2020 | 151 | 2020 |
| Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization JP Miller, R Taori, A Raghunathan, S Sagawa, PW Koh, V Shankar, ... International Conference on Machine Learning, 7721-7735, 2021 | 121 | 2021 |
| DROCC: Deep robust one-class classification S Goyal, A Raghunathan, M Jain, HV Simhadri, P Jain International conference on machine learning, 3711-3721, 2020 | 110 | 2020 |
| An explanation of in-context learning as implicit bayesian inference SM Xie, A Raghunathan, P Liang, T Ma arXiv preprint arXiv:2111.02080, 2021 | 96 | 2021 |
| Robust encodings: A framework for combating adversarial typos E Jones, R Jia, A Raghunathan, P Liang arXiv preprint arXiv:2005.01229, 2020 | 88 | 2020 |
| Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming S Dathathri, K Dvijotham, A Kurakin, A Raghunathan, J Uesato, RR Bunel, ... Advances in Neural Information Processing Systems 33, 5318-5331, 2020 | 67 | 2020 |
| Decoupling exploration and exploitation for meta-reinforcement learning without sacrifices EZ Liu, A Raghunathan, P Liang, C Finn International conference on machine learning, 6925-6935, 2021 | 32 | 2021 |
| Estimating the unseen from multiple populations A Raghunathan, G Valiant, J Zou International Conference on Machine Learning, 2855-2863, 2017 | 18 | 2017 |
| Agreement-on-the-line: Predicting the performance of neural networks under distribution shift C Baek, Y Jiang, A Raghunathan, JZ Kolter Advances in Neural Information Processing Systems 35, 19274-19289, 2022 | 15 | 2022 |
| Maximum weighted loss discrepancy F Khani, A Raghunathan, P Liang arXiv preprint arXiv:1906.03518, 2019 | 12 | 2019 |
