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 | 2822 | 2021 |
Removing spurious features can hurt accuracy and affect groups disproportionately F Khani, P Liang Proceedings of the 2021 ACM conference on fairness, accountability, and …, 2021 | 66 | 2021 |
On the opportunities and risks of foundation models. arXiv 2021 R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2023 | 61 | 2023 |
In-n-out: Pre-training and self-training using auxiliary information for out-of-distribution robustness SM Xie, A Kumar, R Jones, F Khani, T Ma, P Liang arXiv preprint arXiv:2012.04550, 2020 | 51 | 2020 |
& Liang, P.(2021). 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, 0 | 36 | |
On the opportunities and risks of foundation models (2021) R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 0 | 35 | |
Unanimous prediction for 100% precision with application to learning semantic mappings F Khani, M Rinard, P Liang arXiv preprint arXiv:1606.06368, 2016 | 31 | 2016 |
Feature noise induces loss discrepancy across groups F Khani, P Liang International Conference on Machine Learning, 5209-5219, 2020 | 27* | 2020 |
Masktune: Mitigating spurious correlations by forcing to explore S Asgari, A Khani, F Khani, A Gholami, L Tran, A Mahdavi Amiri, ... Advances in Neural Information Processing Systems 35, 23284-23296, 2022 | 25 | 2022 |
Planning, Inference and Pragmatics in Sequential Language Games F Khani, ND Goodman, P Liang Transactions of the Association for Computational Linguistics 6, 543-555, 2018 | 23 | 2018 |
Maximum weighted loss discrepancy F Khani, A Raghunathan, P Liang arXiv preprint arXiv:1906.03518, 2019 | 19 | 2019 |
Prompt engineering a prompt engineer Q Ye, M Axmed, R Pryzant, F Khani arXiv preprint arXiv:2311.05661, 2023 | 8 | 2023 |
Targeted data generation: Finding and fixing model weaknesses Z He, MT Ribeiro, F Khani arXiv preprint arXiv:2305.17804, 2023 | 8 | 2023 |
An algorithm for discovering clusters of different densities or shapes in noisy data sets F Khani, MJ Hosseini, AA Abin, H Beigy Proceedings of the 28th Annual ACM Symposium on Applied Computing, 144-149, 2013 | 7 | 2013 |
Collaborative Alignment of NLP models F Khani, MT Ribeiro Advances in Neural Information Processing Systems 36, 2024 | 2* | 2024 |
Counterbalancing Teacher: Regularizing Batch Normalized Models for Robustness SA Taghanaki, A Gholami, F Khani, K Choi, L Tran, R Zhang, A Khani arXiv preprint arXiv:2207.01548, 2022 | | 2022 |
Causes, Measurement, and Mitigation of Loss Discrepancy F Khani Stanford University, 2021 | | 2021 |
Learning precise partial semantic mappings via linear algebra F Khani Massachusetts Institute of Technology, 2016 | | 2016 |
On the opportunities and risks of foundation models (2021) R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 0 | | |