Training Private Models That Know What They Don't Know

28 May 2023

Papers citing "Training Private Models That Know What They Don't Know"

7 / 7 papers shown

Title
I Know What I Don't Know: Improving Model Cascades Through Confidence Tuning Stephan Rabanser Nathalie Rauschmayr Achin Kulshrestha Petra Poklukar Wittawat Jitkrittum Sean Augenstein Congchao Wang Federico Tombari 42 0 0 26 Feb 2025
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation Gavin Brown Krishnamurthy Dvijotham Georgina Evans Daogao Liu Adam D. Smith Abhradeep Thakurta 28 3 0 21 Feb 2024
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD Anvith Thudi Hengrui Jia Casey Meehan Ilia Shumailov Nicolas Papernot 12 3 0 01 Jul 2023
Differentially Private Sampling from Rashomon Sets, and the Universality of Langevin Diffusion for Convex Optimization Arun Ganesh Abhradeep Thakurta Jalaj Upadhyay 6 1 0 04 Apr 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 138 347 0 25 Sep 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,635 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,042 0 06 Jun 2015