Coupling public and private gradient provably helps optimization

Coupling public and private gradient provably helps optimization

2 October 2023

Sheng Zha

George Karypis

Papers citing "Coupling public and private gradient provably helps optimization"

7 / 7 papers shown

Title
Pre-training Differentially Private Models with Limited Public Data Zhiqi Bu Xinwei Zhang Mingyi Hong Sheng Zha George Karypis 79 3 0 28 Feb 2024
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy Zhiqi Bu Jialin Mao Shiyun Xu 139 48 0 21 May 2022
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 134 350 0 13 Oct 2021
Hyperparameter Tuning with Renyi Differential Privacy Nicolas Papernot Thomas Steinke 135 120 0 07 Oct 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning Da Yu Huishuai Zhang Wei Chen Tie-Yan Liu FedML SILM 94 110 0 25 Feb 2021
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 290 1,824 0 14 Dec 2020
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 299 6,984 0 20 Apr 2018