| 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 | 4821 | 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 | 78 | 2021 |
| 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 | 74 | |
| 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 | 62 | 2020 |
| Prompt engineering a prompt engineer Q Ye, M Axmed, R Pryzant, F Khani arXiv preprint arXiv:2311.05661, 2023 | 58 | 2023 |
| 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 | 44* | 2022 |
| Unanimous prediction for 100% precision with application to learning semantic mappings F Khani, M Rinard, P Liang arXiv preprint arXiv:1606.06368, 2016 | 35 | 2016 |
| Feature noise induces loss discrepancy across groups F Khani, P Liang International Conference on Machine Learning, 5209-5219, 2020 | 34* | 2020 |
| 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 | 25 | 2018 |
| Maximum weighted loss discrepancy F Khani, A Raghunathan, P Liang arXiv preprint arXiv:1906.03518, 2019 | 22 | 2019 |
| Targeted data generation: Finding and fixing model weaknesses Z He, MT Ribeiro, F Khani arXiv preprint arXiv:2305.17804, 2023 | 16 | 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 | 8 | 2013 |
| Collaborative Alignment of NLP models F Khani, MT Ribeiro Advances in Neural Information Processing Systems 36, 2024 | 4* | 2024 |
| Intelligent system and method of optimizing natural language processing models F Khani, Z He, MTC RIBEIRO US Patent App. 18/311,439, 2024 | | 2024 |
| Collaborative development of machine learning models on specific concepts F Khani, MTC RIBEIRO US Patent App. 18/776,709, 2024 | | 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 | | |
Percy LiangAssociate Professor of Computer Science, Stanford UniversityAdresse e-mail validée de cs.stanford.edu
