{"user_id":"15504","author":[{"full_name":"Senge, Manuel","last_name":"Senge","first_name":"Manuel"},{"full_name":"Igamberdiev, Timour","last_name":"Igamberdiev","first_name":"Timour"},{"first_name":"Ivan","id":"101881","last_name":"Habernal","full_name":"Habernal, Ivan"}],"year":"2023","status":"public","_id":"48297","publication_status":"published","date_created":"2023-10-19T08:26:21Z","doi":"10.18653/v1/2022.emnlp-main.496","department":[{"_id":"34"},{"_id":"820"}],"citation":{"bibtex":"@inproceedings{Senge_Igamberdiev_Habernal_2023, title={One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks}, DOI={<a href=\"https://doi.org/10.18653/v1/2022.emnlp-main.496\">10.18653/v1/2022.emnlp-main.496</a>}, 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} }","ieee":"M. Senge, T. Igamberdiev, and I. Habernal, “One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks,” 2023, doi: <a href=\"https://doi.org/10.18653/v1/2022.emnlp-main.496\">10.18653/v1/2022.emnlp-main.496</a>.","ama":"Senge M, Igamberdiev T, Habernal I. One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks. In: <i>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</i>. Association for Computational Linguistics; 2023. doi:<a href=\"https://doi.org/10.18653/v1/2022.emnlp-main.496\">10.18653/v1/2022.emnlp-main.496</a>","short":"M. Senge, T. Igamberdiev, I. Habernal, in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2023.","apa":"Senge, M., Igamberdiev, T., &#38; Habernal, I. (2023). One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks. <i>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</i>. <a href=\"https://doi.org/10.18653/v1/2022.emnlp-main.496\">https://doi.org/10.18653/v1/2022.emnlp-main.496</a>","mla":"Senge, Manuel, et al. “One Size Does Not Fit All: Investigating Strategies for Differentially-Private Learning across NLP Tasks.” <i>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</i>, Association for Computational Linguistics, 2023, doi:<a href=\"https://doi.org/10.18653/v1/2022.emnlp-main.496\">10.18653/v1/2022.emnlp-main.496</a>.","chicago":"Senge, Manuel, Timour Igamberdiev, and Ivan Habernal. “One Size Does Not Fit All: Investigating Strategies for Differentially-Private Learning across NLP Tasks.” In <i>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</i>. Association for Computational Linguistics, 2023. <a href=\"https://doi.org/10.18653/v1/2022.emnlp-main.496\">https://doi.org/10.18653/v1/2022.emnlp-main.496</a>."},"type":"conference","publisher":"Association for Computational Linguistics","date_updated":"2023-10-19T12:05:55Z","language":[{"iso":"eng"}],"publication":"Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing","title":"One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks"}