A Closer Look at the Calibration of Differentially Private Learners

A Closer Look at the Calibration of Differentially Private Learners

15 October 2022

Tatsunori Hashimoto

Papers citing "A Closer Look at the Calibration of Differentially Private Learners"

8 / 8 papers shown

Title
Selective Pre-training for Private Fine-tuning Da Yu Sivakanth Gopi Janardhan Kulkarni Zi-Han Lin Saurabh Naik Tomasz Religa Jian Yin Huishuai Zhang 30 19 0 23 May 2023
Equivariant Differentially Private Deep Learning: Why DP-SGD Needs Sparser Models Florian A. Hölzl Daniel Rueckert Georgios Kaissis 21 4 0 30 Jan 2023
The Calibration Generalization Gap Annabelle Carrell Neil Rohit Mallinar James Lucas Preetum Nakkiran UQCV 32 18 0 05 Oct 2022
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 144 349 0 25 Sep 2021
On the Convergence and Calibration of Deep Learning with Differential Privacy Zhiqi Bu Hua Wang Zongyu Dai Qi Long 17 27 0 15 Jun 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 267 1,812 0 14 Dec 2020
Calibration of Pre-trained Transformers Shrey Desai Greg Durrett UQLM 243 289 0 17 Mar 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 294 6,950 0 20 Apr 2018