One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks
M. Senge, T. Igamberdiev, I. Habernal, in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2023.
Download
No fulltext has been uploaded.
Conference Paper
| Published
| English
Author
Senge, Manuel;
Igamberdiev, Timour;
Habernal, IvanLibreCat
Publishing Year
Proceedings Title
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
LibreCat-ID
Cite this
Senge M, Igamberdiev T, Habernal I. One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics; 2023. doi:10.18653/v1/2022.emnlp-main.496
Senge, M., Igamberdiev, T., & Habernal, I. (2023). One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/2022.emnlp-main.496
@inproceedings{Senge_Igamberdiev_Habernal_2023, title={One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks}, DOI={10.18653/v1/2022.emnlp-main.496}, booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing}, publisher={Association for Computational Linguistics}, author={Senge, Manuel and Igamberdiev, Timour and Habernal, Ivan}, year={2023} }
Senge, Manuel, Timour Igamberdiev, and Ivan Habernal. “One Size Does Not Fit All: Investigating Strategies for Differentially-Private Learning across NLP Tasks.” In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2023. https://doi.org/10.18653/v1/2022.emnlp-main.496.
M. Senge, T. Igamberdiev, and I. Habernal, “One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks,” 2023, doi: 10.18653/v1/2022.emnlp-main.496.
Senge, Manuel, et al. “One Size Does Not Fit All: Investigating Strategies for Differentially-Private Learning across NLP Tasks.” Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2023, doi:10.18653/v1/2022.emnlp-main.496.
Google Scholar