Arun Ganesh

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 39

Top Topics

Convex Optimization

Stationary Points

Differentially Private

Top Venues

ACM Trans. Algorithms

Publications

Christopher A. Choquette-Choo, Arun Ganesh, Abhradeep Thakurta
Optimal Rates for DP-SCO with a Single Epoch and Large Batches. CoRR (2024)
Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush
Fine-Tuning Large Language Models with User-Level Differential Privacy. CoRR (2024)
Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta
Privacy Amplification for Matrix Mechanisms. ICLR (2024)
Arun Ganesh
Tight Group-Level DP Guarantees for DP-SGD with Sampling via Mixture of Gaussians Mechanisms. CoRR (2024)
Christopher A. Choquette-Choo, Krishnamurthy Dj Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. ICLR (2024)
Arun Ganesh, Mahdi Haghifam, Thomas Steinke, Abhradeep Thakurta
Faster Differentially Private Convex Optimization via Second-Order Methods. CoRR (2023)
Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, Lun Wang
Why Is Public Pretraining Necessary for Private Model Training? ICML (2023)
Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, John Rush, Abhradeep Guha Thakurta, Zheng Xu
(Amplified) Banded Matrix Factorization: A unified approach to private training. NeurIPS (2023)
Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Thakurta, Lun Wang
Why Is Public Pretraining Necessary for Private Model Training? CoRR (2023)
Arun Ganesh, Mahdi Haghifam, Thomas Steinke, Abhradeep Guha Thakurta
Faster Differentially Private Convex Optimization via Second-Order Methods. NeurIPS (2023)
Arun Ganesh, Daogao Liu, Sewoong Oh, Abhradeep Thakurta
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks. CoRR (2023)
Arun Ganesh, Bruce M. Maggs, Debmalya Panigrahi
Robust Algorithms for TSP and Steiner Tree. ACM Trans. Algorithms 19 (2) (2023)
Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Thakurta
Privacy Amplification for Matrix Mechanisms. CoRR (2023)
Arun Ganesh, Bruce M. Maggs, Debmalya Panigrahi
Universal Algorithms for Clustering Problems. ACM Trans. Algorithms 19 (2) (2023)
Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu
(Amplified) Banded Matrix Factorization: A unified approach to private training. CoRR (2023)
Daogao Liu, Arun Ganesh, Sewoong Oh, Abhradeep Guha Thakurta
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks. NeurIPS (2023)
Arun Ganesh, Abhradeep Thakurta, Jalaj Upadhyay
Universality of Langevin Diffusion for Private Optimization, with Applications to Sampling from Rashomon Sets. COLT (2023)
Christopher A. Choquette-Choo, Krishnamurthy Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Thakurta
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. CoRR (2023)
Virat Shejwalkar, Arun Ganesh, Rajiv Mathews, Om Thakkar, Abhradeep Thakurta
Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints. CoRR (2022)
Arun Ganesh, Abhradeep Thakurta, Jalaj Upadhyay
Langevin Diffusion: An Almost Universal Algorithm for Private Euclidean (Convex) Optimization. CoRR (2022)
Arun Ganesh, Tomasz Kociumaka, Andrea Lincoln, Barna Saha
How Compression and Approximation Affect Efficiency in String Distance Measures. SODA (2022)
Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta
Public Data-Assisted Mirror Descent for Private Model Training. ICML (2022)
Arun Ganesh, Jiazheng Zhao
Privately Answering Counting Queries with Generalized Gaussian Mechanisms. FORC (2021)
Arun Ganesh, Tomasz Kociumaka, Andrea Lincoln, Barna Saha
How Compression and Approximation Affect Efficiency in String Distance Measures. CoRR (2021)
Arun Ganesh, Bruce M. Maggs, Debmalya Panigrahi
Universal Algorithms for Clustering. CoRR (2021)
Arun Ganesh, Bruce M. Maggs, Debmalya Panigrahi
Universal Algorithms for Clustering Problems. ICALP (2021)
Yossi Azar, Arun Ganesh, Rong Ge, Debmalya Panigrahi
Online Service with Delay. ACM Trans. Algorithms 17 (3) (2021)
Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta
Public Data-Assisted Mirror Descent for Private Model Training. CoRR (2021)
Arun Ganesh, Aaron Sy
Near-Linear Time Edit Distance for Indel Channels. WABI (2020)
Arun Ganesh, Bruce M. Maggs, Debmalya Panigrahi
Robust Algorithms for TSP and Steiner Tree. ICALP (2020)
Arun Ganesh, Jiazheng Zhao
Privately Answering Counting Queries with Generalized Gaussian Mechanisms. CoRR (2020)
Arun Ganesh, Kunal Talwar
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC. CoRR (2020)
Arun Ganesh, Kunal Talwar
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC. NeurIPS (2020)
Arun Ganesh, Aaron Sy
Near-Linear Time Edit Distance for Indel Channels. CoRR (2020)
Arun Ganesh, Bruce M. Maggs, Debmalya Panigrahi
Robust Algorithms for TSP and Steiner Tree. CoRR (2020)
Arun Ganesh, Qiuyi (Richard) Zhang
Optimal sequence length requirements for phylogenetic tree reconstruction with indels. STOC (2019)
Arun Ganesh, Qiuyi (Richard) Zhang
Optimal Sequence Length Requirements for Phylogenetic Tree Reconstruction with Indels. CoRR (2018)
Yossi Azar, Arun Ganesh, Rong Ge, Debmalya Panigrahi
Online service with delay. STOC (2017)
Yossi Azar, Arun Ganesh, Rong Ge, Debmalya Panigrahi
Online Service with Delay. CoRR (2017)