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Fereshte Khani
Fereshte Khani
OpenAI
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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
28872021
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
662021
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
632023
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
512020
& 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
38
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
37
Unanimous prediction for 100% precision with application to learning semantic mappings
F Khani, M Rinard, P Liang
arXiv preprint arXiv:1606.06368, 2016
312016
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
262022
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
232018
Maximum weighted loss discrepancy
F Khani, A Raghunathan, P Liang
arXiv preprint arXiv:1906.03518, 2019
192019
Prompt engineering a prompt engineer
Q Ye, M Axmed, R Pryzant, F Khani
arXiv preprint arXiv:2311.05661, 2023
82023
Targeted data generation: Finding and fixing model weaknesses
Z He, MT Ribeiro, F Khani
arXiv preprint arXiv:2305.17804, 2023
82023
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
72013
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
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