fereshte khani
Title
Cited by
Cited by
Year
Unanimous prediction for 100% precision with application to learning semantic mappings
F Khani, M Rinard, P Liang
arXiv preprint arXiv:1606.06368, 2016
15*2016
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
112018
Maximum weighted loss discrepancy
F Khani, A Raghunathan, P Liang
arXiv preprint arXiv:1906.03518, 2019
62019
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
52020
Feature noise induces loss discrepancy across groups
F Khani, P Liang
International Conference on Machine Learning, 5209-5219, 2020
52020
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
52013
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
42021
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
42021
Learning precise partial semantic mappings via linear algebra
F Khani
Massachusetts Institute of Technology, 2016
2016
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Articles 1–9