Optimal sequence length requirements for phylogenetic tree reconstruction with indels. - Arun Ganesh; Qiuyi (Richard) Zhang (STOC, 2019)

Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC. - Arun Ganesh; Kunal Talwar (NeurIPS, 2020)

Near-Linear Time Edit Distance for Indel Channels. - Arun Ganesh; Aaron Sy (WABI, 2020)

Privately Answering Counting Queries with Generalized Gaussian Mechanisms. - Arun Ganesh; Jiazheng Zhao (FORC, 2021)

Universal Algorithms for Clustering. - Arun Ganesh; Bruce M. Maggs; Debmalya Panigrahi (CoRR, 2021)

Online Service with Delay. - Yossi Azar; Arun Ganesh; Rong Ge 001; Debmalya Panigrahi (ACM Trans. Algorithms, 2021)

Public Data-Assisted Mirror Descent for Private Model Training. - Ehsan Amid; Arun Ganesh; Rajiv Mathews; Swaroop Ramaswamy; Shuang Song 001; Thomas Steinke 002; Vinith M. Suriyakumar; Om Thakkar 001; Abhradeep Thakurta (ICML, 2022)

How Compression and Approximation Affect Efficiency in String Distance Measures. - Arun Ganesh; Tomasz Kociumaka; Andrea Lincoln; Barna Saha (SODA, 2022)

Langevin Diffusion: An Almost Universal Algorithm for Private Euclidean (Convex) Optimization. - Arun Ganesh; Abhradeep Thakurta; Jalaj Upadhyay (CoRR, 2022)

Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints. - Virat Shejwalkar; Arun Ganesh; Rajiv Mathews; Om Thakkar 001; Abhradeep Thakurta (CoRR, 2022)

Improved Algorithms and Upper Bounds in Differential Privacy - Arun Ganesh (2022)

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

Why Is Public Pretraining Necessary for Private Model Training? - Arun Ganesh; Mahdi Haghifam; Milad Nasr; Sewoong Oh; Thomas Steinke 002; Om Thakkar 001; Abhradeep Guha Thakurta; Lun Wang 001 (ICML, 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 002 (NeurIPS, 2023)

Faster Differentially Private Convex Optimization via Second-Order Methods. - Arun Ganesh; Mahdi Haghifam; Thomas Steinke 002; 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 (NeurIPS, 2023)

(Amplified) Banded Matrix Factorization: A unified approach to private training. - Christopher A. Choquette-Choo; Arun Ganesh; Ryan McKenna; H. Brendan McMahan; Keith Rush; Abhradeep Guha Thakurta; Zheng Xu 002 (CoRR, 2023)

Robust Algorithms for TSP and Steiner Tree. - Arun Ganesh; Bruce M. Maggs; Debmalya Panigrahi (ACM Trans. Algorithms, 2023)

Universal Algorithms for Clustering Problems. - Arun Ganesh; Bruce M. Maggs; Debmalya Panigrahi (ACM Trans. Algorithms, 2023)

Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. - Christopher A. Choquette-Choo; Krishnamurthy Dj Dvijotham; Krishna Pillutla; Arun Ganesh; Thomas Steinke 002; Abhradeep Guha Thakurta (ICLR, 2024)

Privacy Amplification for Matrix Mechanisms. - Christopher A. Choquette-Choo; Arun Ganesh; Thomas Steinke 002; Abhradeep Guha Thakurta (ICLR, 2024)

Tight Group-Level DP Guarantees for DP-SGD with Sampling via Mixture of Gaussians Mechanisms. - Arun Ganesh (CoRR, 2024)

Optimal Rates for DP-SCO with a Single Epoch and Large Batches. - Christopher A. Choquette-Choo; Arun Ganesh; Abhradeep Thakurta (CoRR, 2024)

Fine-Tuning Large Language Models with User-Level Differential Privacy. - Zachary Charles; Arun Ganesh; Ryan McKenna; H. Brendan McMahan; Nicole Mitchell; Krishna Pillutla; Keith Rush (CoRR, 2024)

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 (ALT, 2025)

Near-Exact Privacy Amplification for Matrix Mechanisms. - Christopher A. Choquette-Choo; Arun Ganesh; Saminul Haque; Thomas Steinke 002; Abhradeep Guha Thakurta (ICLR, 2025)

The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD. - Milad Nasr; Thomas Steinke 002; Borja Balle; Christopher A. Choquette-Choo; Arun Ganesh; Matthew Jagielski; Jamie Hayes; Abhradeep Guha Thakurta; Adam Smith 006; Andreas Terzis (ICLR, 2025)

Learning with User-Level Differential Privacy Under Fixed Compute Budgets. - Zachary Charles; Arun Ganesh; Ryan McKenna; H. Brendan McMahan; Nicole Mitchell; Krishna Pillutla; Keith Rush (SaTML, 2025)

Recycling Scraps: Improving Private Learning by Leveraging Checkpoints]{Recycling Scraps: Improving Private Learning by Leveraging Checkpoints. - Virat Shejwalkar; Arun Ganesh; Rajiv Mathews; Yarong Mu; Shuang Song 001; Om Thakkar 001; Abhradeep Thakurta; Xinyi Zheng (Proc. Priv. Enhancing Technol., 2025)

It's My Data Too: Private ML for Datasets with Multi-User Training Examples. - Arun Ganesh; Ryan McKenna; Brendan McMahan; Adam Smith 006; Fan Wu (CoRR, 2025)

Hush! Protecting Secrets During Model Training: An Indistinguishability Approach. - Arun Ganesh; Brendan McMahan; Milad Nasr; Thomas Steinke 002; Abhradeep Thakurta (CoRR, 2025)

Correlated Noise Mechanisms for Differentially Private Learning. - Krishna Pillutla; Jalaj Upadhyay; Christopher A. Choquette-Choo; Krishnamurthy Dvijotham; Arun Ganesh; Monika Henzinger; Jonathan Katz; Ryan McKenna; H. Brendan McMahan; Keith Rush; Thomas Steinke 002; Abhradeep Thakurta (CoRR, 2025)

On Design Principles for Private Adaptive Optimizers. - Arun Ganesh; Brendan McMahan; Abhradeep Thakurta (CoRR, 2025)