| 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 | 3213 | 2021 |
| Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization S Sagawa*, PW Koh*, TB Hashimoto, P Liang International Conference on Learning Representations, 2019 | 1499 | 2019 |
| WILDS: A benchmark of in-the-wild distribution shifts PW Koh*, S Sagawa*, H Marklund, SM Xie, M Zhang, A Balsubramani, ... International Conference on Machine Learning, 5637-5664, 2021 | 1249 | 2021 |
| 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 | 425 | 2021 |
| An investigation of why overparameterization exacerbates spurious correlations S Sagawa*, A Raghunathan*, PW Koh*, P Liang International Conference on Machine Learning, 8346-8356, 2020 | 344 | 2020 |
| Openflamingo: An open-source framework for training large autoregressive vision-language models A Awadalla, I Gao, J Gardner, J Hessel, Y Hanafy, W Zhu, K Marathe, ... arXiv preprint arXiv:2308.01390, 2023 | 258 | 2023 |
| 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 | 247 | 2021 |
| Distributionally robust language modeling Y Oren*, S Sagawa*, TB Hashimoto*, P Liang arXiv preprint arXiv:1909.02060, 2019 | 175 | 2019 |
| Extending the WILDS Benchmark for Unsupervised Adaptation S Sagawa*, PW Koh*, T Lee*, I Gao*, SM Xie, K Shen, A Kumar, W Hu, ... International Conference on Learning Representations, 2022 | 107 | 2022 |
| The architecture of EGFR’s basal complexes reveals autoinhibition mechanisms in dimers and oligomers LC Zanetti-Domingues, D Korovesis, SR Needham, CJ Tynan, S Sagawa, ... Nature communications 9 (1), 4325, 2018 | 76 | 2018 |
| Structural mechanism for Bruton’s tyrosine kinase activation at the cell membrane Q Wang, Y Pechersky, S Sagawa, AC Pan, DE Shaw Proceedings of the National Academy of Sciences 116 (19), 9390-9399, 2019 | 58 | 2019 |
| Selective classification can magnify disparities across groups E Jones, S Sagawa, PW Koh, A Kumar, P Liang International Conference on Learning Representations, 2020 | 49 | 2020 |
| Multi-resolution weak supervision for sequential data P Varma, F Sala, S Sagawa, J Fries, D Fu, S Khattar, A Ramamoorthy, ... Advances in Neural Information Processing Systems 32, 2019 | 39 | 2019 |
| Genotype specification language EH Wilson*, S Sagawa*, JW Weis*, MG Schubert*, M Bissell, ... ACS synthetic biology 5 (6), 471-478, 2016 | 34 | 2016 |
| Out-of-Domain Robustness via Targeted Augmentations I Gao*, S Sagawa*, PW Koh, T Hashimoto, P Liang International Conference on Machine Learning, 2023 | 30* | 2023 |
| How does a small molecule bind at a cryptic binding site? Y Shan, VP Mysore, AE Leffler, ET Kim, S Sagawa, DE Shaw PLoS computational biology 18 (3), e1009817, 2022 | 25 | 2022 |
| Modulating the frequency and bias of stochastic switching to control phenotypic variation M Hung, E Chang, R Hussein, K Frazier, JE Shin, S Sagawa, HN Lim Nature Communications 5 (1), 4574, 2014 | 24 | 2014 |
| Paradoxical suppression of small RNA activity at high Hfq concentrations due to random-order binding S Sagawa*, JE Shin*, R Hussein, HN Lim Nucleic acids research 43 (17), 8502-8515, 2015 | 22 | 2015 |
| Validating regulatory predictions from diverse bacteria with mutant fitness data S Sagawa, MN Price, AM Deutschbauer, AP Arkin PloS one 12 (5), e0178258, 2017 | 10 | 2017 |
