Exploring the Benefits of Differentially Private Pre-training and Parameter-Efficient Fine-tuning for Table Transformers

12 September 2023

Papers citing "Exploring the Benefits of Differentially Private Pre-training and Parameter-Efficient Fine-tuning for Table Transformers"

3 / 3 papers shown

Title
Differentially Private Fine-tuning of Language Models Da Yu Saurabh Naik A. Backurs Sivakanth Gopi Huseyin A. Inan ... Y. Lee Andre Manoel Lukas Wutschitz Sergey Yekhanin Huishuai Zhang 134 347 0 13 Oct 2021
Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication Muah Kim Onur Gunlu Rafael F. Schaefer FedML 110 118 0 09 Feb 2021
TabTransformer: Tabular Data Modeling Using Contextual Embeddings Xin Huang A. Khetan Milan Cvitkovic Zohar Karnin ViT LMTD 157 417 0 11 Dec 2020