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Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe.

, , , , , , , , and . ACL (1), page 1321-1342. Association for Computational Linguistics, (2023)

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On Privacy and Confidentiality of Communications in Organizational Graphs., , , and . CoRR, (2021)Privately Aligning Language Models with Reinforcement Learning., , , , , and . CoRR, (2023)When Does Differentially Private Learning Not Suffer in High Dimensions?, , , , , , and . NeurIPS, (2022)Membership Inference on Word Embedding and Beyond., , , , and . CoRR, (2021)Sparse group testing codes for low-energy massive random access., , and . Allerton, page 658-665. IEEE, (2017)Differentially Private Fine-tuning of Language Models., , , , , , , , , and 2 other author(s). ICLR, OpenReview.net, (2022)Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe., , , , , , , , and . ACL (1), page 1321-1342. Association for Computational Linguistics, (2023)Energy-limited Massive Random Access via Noisy Group Testing., , and . ISIT, page 1101-1105. IEEE, (2018)rTop-k: A Statistical Estimation Approach to Distributed SGD., , , and . IEEE J. Sel. Areas Inf. Theory, 1 (3): 897-907 (2020)Capacity of the Energy Harvesting Gaussian MAC., , and . IEEE Trans. Inf. Theory, 64 (4): 2347-2360 (2018)