Practical Differentially Private Hyperparameter Tuning with Subsampling

27 January 2023

Papers citing "Practical Differentially Private Hyperparameter Tuning with Subsampling"

17 / 17 papers shown

Title
The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data Georgi Ganev Meenatchi Sundaram Muthu Selva Annamalai Sofiane Mahiou Emiliano De Cristofaro 24 2 0 09 Apr 2025
Towards hyperparameter-free optimization with differential privacy Zhiqi Bu Ruixuan Liu 32 2 0 02 Mar 2025
LNUCB-TA: Linear-nonlinear Hybrid Bandit Learning with Temporal Attention H. Khosravi Mohammad Reza Shafie Ahmed Shoyeb Raihan Srinjoy Das I. Imtiaz Ahmed 34 0 0 01 Mar 2025
DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning Yixuan Liu Li Xiong Yuhan Liu Yujie Gu Ruixuan Liu Hong Chen 40 1 0 04 Jun 2024
How to Privately Tune Hyperparameters in Federated Learning? Insights from a Benchmark Study Natalija Mitic Apostolos Pyrgelis Sinem Sav FedML 58 1 0 25 Feb 2024
Revisiting Differentially Private Hyper-parameter Tuning Zihang Xiang Tianhao Wang Cheng-Long Wang Di Wang 34 6 0 20 Feb 2024
On the Impact of Output Perturbation on Fairness in Binary Linear Classification Vitalii Emelianov Michael Perrot FaML 35 0 0 05 Feb 2024
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy T. Shaik Xiaohui Tao Haoran Xie Lin Li Xiaofeng Zhu Qingyuan Li MU 36 25 0 10 May 2023
Improved Differentially Private Regression via Gradient Boosting Shuai Tang Sergul Aydore Michael Kearns Saeyoung Rho Aaron Roth Yichen Wang Yu-Xiang Wang Zhiwei Steven Wu FedML 38 4 0 06 Mar 2023
Composition of Differential Privacy & Privacy Amplification by Subsampling Thomas Steinke 64 49 0 02 Oct 2022
The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection Shubhankar Mohapatra Sajin Sasy Xi He Gautam Kamath Om Thakkar 114 32 0 09 Nov 2021
Hyperparameter Tuning with Renyi Differential Privacy Nicolas Papernot Thomas Steinke 135 120 0 07 Oct 2021
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 168 350 0 25 Sep 2021
Making the Most of Parallel Composition in Differential Privacy Joshua Smith Hassan Jameel Asghar Gianpaolo Gioiosa Sirine Mrabet Serge Gaspers P. Tyler 28 10 0 19 Sep 2021
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training Krishnateja Killamsetty D. Sivasubramanian Ganesh Ramakrishnan A. De Rishabh K. Iyer OOD 94 189 0 27 Feb 2021
Practical and Private (Deep) Learning without Sampling or Shuffling Peter Kairouz Brendan McMahan Shuang Song Om Thakkar Abhradeep Thakurta Zheng Xu FedML 182 154 0 26 Feb 2021
Tempered Sigmoid Activations for Deep Learning with Differential Privacy Nicolas Papernot Abhradeep Thakurta Shuang Song Steve Chien Ulfar Erlingsson AAML 147 178 0 28 Jul 2020