Ganesh, Arun

15 publications

ICML 2025 It’s My Data Too: Private ML for Datasets with Multi-User Training Examples Arun Ganesh, Ryan Mckenna, Hugh Brendan Mcmahan, Adam Smith, Fan Wu

ICLR 2025 Near-Exact Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo, Arun Ganesh, Saminul Haque, Thomas Steinke, Abhradeep Guha Thakurta

ALT 2025 Near-Optimal Rates for O(1)-Smooth DP-SCO with a Single Epoch and Large Batches Christopher A. Choquette-Choo, Arun Ganesh, Abhradeep Guha Thakurta

ICLR 2025 The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD Milad Nasr, Thomas Steinke, Borja Balle, Christopher A. Choquette-Choo, Arun Ganesh, Matthew Jagielski, Jamie Hayes, Abhradeep Guha Thakurta, Adam Smith, Andreas Terzis

ICLR 2024 Correlated Noise Provably Beats Independent Noise for Differentially Private Learning Christopher A. Choquette-Choo, Krishnamurthy Dj Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta

ICMLW 2024 Fine-Tuning Large Language Models with User-Level Differential Privacy Zachary Charles, Arun Ganesh, Ryan McKenna, Hugh Brendan McMahan, Nicole Elyse Mitchell, Krishna Pillutla, J Keith Rush

ICLR 2024 Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta

NeurIPS 2023 (Amplified) Banded Matrix Factorization: A Unified Approach to Private Training Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, John Rush, Abhradeep Guha Thakurta, Zheng Xu

NeurIPSW 2023 Correlated Noise Provably Beats Independent Noise for Differentially Private Learning Christopher A. Choquette-Choo, Krishnamurthy Dj Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta

NeurIPS 2023 Faster Differentially Private Convex Optimization via Second-Order Methods Arun Ganesh, Mahdi Haghifam, Thomas Steinke, Abhradeep Guha Thakurta

NeurIPS 2023 Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks Daogao Liu, Arun Ganesh, Sewoong Oh, Abhradeep Guha Thakurta

COLT 2023 Universality of Langevin Diffusion for Private Optimization, with Applications to Sampling from Rashomon Sets Arun Ganesh, Abhradeep Thakurta, Jalaj Upadhyay

ICML 2023 Why Is Public Pretraining Necessary for Private Model Training? Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, Lun Wang

ICML 2022 Public Data-Assisted Mirror Descent for Private Model Training Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Thomas Steinke, Vinith M Suriyakumar, Om Thakkar, Abhradeep Thakurta

NeurIPS 2020 Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC Arun Ganesh, Kunal Talwar