(Amplified) Banded Matrix Factorization: A unified approach to private
training

(Amplified) Banded Matrix Factorization: A unified approach to private training

13 June 2023

Christopher A. Choquette-Choo

Abhradeep Thakurta

Papers citing "(Amplified) Banded Matrix Factorization: A unified approach to private training"

18 / 18 papers shown

Title
Back to Square Roots: An Optimal Bound on the Matrix Factorization Error for Multi-Epoch Differentially Private SGD Nikita P. Kalinin Ryan McKenna Jalaj Upadhyay Christoph H. Lampert 7 0 0 17 May 2025
An Inversion Theorem for Buffered Linear Toeplitz (BLT) Matrices and Applications to Streaming Differential Privacy H. B. McMahan Krishna Pillutla 36 1 0 30 Apr 2025
Binned Group Algebra Factorization for Differentially Private Continual Counting Monika Henzinger Nikita P. Kalinin Jalaj Upadhyay 31 2 0 06 Apr 2025
Investigating Large Language Models in Diagnosing Students' Cognitive Skills in Math Problem-solving Hyoungwook Jin Yoonsu Kim Dongyun Jung Seungju Kim Kiyoon Choi J. Son Juho Kim LRM 62 0 0 01 Apr 2025
Near Exact Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Saminul Haque Thomas Steinke Abhradeep Thakurta 38 6 0 08 Oct 2024
Debiasing Federated Learning with Correlated Client Participation Zhenyu Sun Ziyang Zhang Zheng Xu Gauri Joshi Pranay Sharma Ermin Wei FedML 29 0 0 02 Oct 2024
A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs H. B. McMahan Zheng Xu Yanxiang Zhang FedML 48 6 0 16 Aug 2024
Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation Sajani Vithana V. Cadambe Flavio du Pin Calmon Haewon Jeong FedML 50 1 0 03 Jul 2024
Click Without Compromise: Online Advertising Measurement via Per User Differential Privacy Yingtai Xiao Jian Du Shikun Zhang Qiang Yan Danfeng Zhang Daniel Kifer Daniel Kifer 51 2 0 04 Jun 2024
Confidential Federated Computations Hubert Eichner Daniel Ramage Kallista A. Bonawitz Dzmitry Huba Tiziano Santoro ... Albert Cheu Katharine Daly Adria Gascon Marco Gruteser Brendan McMahan 50 2 0 16 Apr 2024
Tight Group-Level DP Guarantees for DP-SGD with Sampling via Mixture of Gaussians Mechanisms Arun Ganesh 26 2 0 17 Jan 2024
A Smooth Binary Mechanism for Efficient Private Continual Observation Joel Daniel Andersson Rasmus Pagh 30 12 0 16 Jun 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy Natalia Ponomareva Hussein Hazimeh Alexey Kurakin Zheng Xu Carson E. Denison H. B. McMahan Sergei Vassilvitskii Steve Chien Abhradeep Thakurta 96 167 0 01 Mar 2023
Composition of Differential Privacy & Privacy Amplification by Subsampling Thomas Steinke 66 50 0 02 Oct 2022
Private Convex Optimization via Exponential Mechanism Sivakanth Gopi Y. Lee Daogao Liu 89 52 0 01 Mar 2022
Papaya: Practical, Private, and Scalable Federated Learning Dzmitry Huba John Nguyen Kshitiz Malik Ruiyu Zhu Michael G. Rabbat ... H. Srinivas Kaikai Wang Anthony Shoumikhin Jesik Min Mani Malek FedML 113 137 0 08 Nov 2021
Practical and Private (Deep) Learning without Sampling or Shuffling Peter Kairouz Brendan McMahan Shuang Song Om Thakkar Abhradeep Thakurta Zheng Xu FedML 182 194 0 26 Feb 2021
Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications Matthias Paulik M. Seigel Henry Mason Dominic Telaar Joris Kluivers ... Dominic Hughes O. Javidbakht Fei Dong Rehan Rishi Stanley Hung FedML 183 126 0 16 Feb 2021