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ORCIDArun Ganesh
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2020 – today
- 2024
[c16]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- [c15]Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta:
Privacy Amplification for Matrix Mechanisms. ICLR 2024 - [i22]Arun Ganesh:
Tight Group-Level DP Guarantees for DP-SGD with Sampling via Mixture of Gaussians Mechanisms. CoRR abs/2401.10294 (2024) - [i21]Christopher A. Choquette-Choo, Arun Ganesh, Abhradeep Thakurta:
Optimal Rates for DP-SCO with a Single Epoch and Large Batches. CoRR abs/2406.02716 (2024) - [i20]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 abs/2407.07737 (2024) - [i19]Thomas Steinke, Milad Nasr, Arun Ganesh, Borja Balle, Christopher A. Choquette-Choo, Matthew Jagielski, Jamie Hayes, Abhradeep Guha Thakurta, Adam D. Smith, Andreas Terzis:
The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD. CoRR abs/2410.06186 (2024) - [i18]Christopher A. Choquette-Choo, Arun Ganesh, Saminul Haque, Thomas Steinke, Abhradeep Thakurta:
Near Exact Privacy Amplification for Matrix Mechanisms. CoRR abs/2410.06266 (2024) - 2023
- [j3]Arun Ganesh
, Bruce M. Maggs, Debmalya Panigrahi:
Robust Algorithms for TSP and Steiner Tree. ACM Trans. Algorithms 19(2): 12:1-12:37 (2023) - [j2]Arun Ganesh, Bruce M. Maggs, Debmalya Panigrahi:
Universal Algorithms for Clustering Problems. ACM Trans. Algorithms 19(2): 15:1-15:46 (2023) - [c14]Arun Ganesh, Abhradeep Thakurta, Jalaj Upadhyay:
Universality of Langevin Diffusion for Private Optimization, with Applications to Sampling from Rashomon Sets. COLT 2023: 1730-1773 - [c13]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: 10611-10627 - [c12]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 - [c11]Arun Ganesh, Mahdi Haghifam, Thomas Steinke, Abhradeep Guha Thakurta:
Faster Differentially Private Convex Optimization via Second-Order Methods. NeurIPS 2023 - [c10]Daogao Liu, Arun Ganesh, Sewoong Oh, Abhradeep Guha Thakurta:
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks. NeurIPS 2023 - [i17]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 abs/2302.09483 (2023) - [i16]Arun Ganesh, Daogao Liu, Sewoong Oh, Abhradeep Thakurta:
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks. CoRR abs/2302.09699 (2023) - [i15]Arun Ganesh, Mahdi Haghifam, Thomas Steinke, Abhradeep Thakurta:
Faster Differentially Private Convex Optimization via Second-Order Methods. CoRR abs/2305.13209 (2023) - [i14]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 abs/2306.08153 (2023) - [i13]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 abs/2310.06771 (2023) - [i12]Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Thakurta:
Privacy Amplification for Matrix Mechanisms. CoRR abs/2310.15526 (2023) - 2022
- [b1]Arun Ganesh:
Improved Algorithms and Upper Bounds in Differential Privacy. University of California, Berkeley, USA, 2022 - [c9]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: 517-535 - [c8]Arun Ganesh, Tomasz Kociumaka, Andrea Lincoln, Barna Saha:
How Compression and Approximation Affect Efficiency in String Distance Measures. SODA 2022: 2867-2919 - [i11]Arun Ganesh, Abhradeep Thakurta, Jalaj Upadhyay:
Langevin Diffusion: An Almost Universal Algorithm for Private Euclidean (Convex) Optimization. CoRR abs/2204.01585 (2022) - [i10]Virat Shejwalkar, Arun Ganesh, Rajiv Mathews, Om Thakkar, Abhradeep Thakurta:
Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints. CoRR abs/2210.01864 (2022) - 2021
- [j1]Yossi Azar, Arun Ganesh, Rong Ge, Debmalya Panigrahi:
Online Service with Delay. ACM Trans. Algorithms 17(3): 23:1-23:31 (2021) - [c7]Arun Ganesh, Jiazheng Zhao:
Privately Answering Counting Queries with Generalized Gaussian Mechanisms. FORC 2021: 1:1-1:18 - [c6]Arun Ganesh, Bruce M. Maggs, Debmalya Panigrahi:
Universal Algorithms for Clustering Problems. ICALP 2021: 70:1-70:20 - [i9]Arun Ganesh, Bruce M. Maggs, Debmalya Panigrahi:
Universal Algorithms for Clustering. CoRR abs/2105.02363 (2021) - [i8]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 abs/2112.00193 (2021) - [i7]Arun Ganesh, Tomasz Kociumaka, Andrea Lincoln, Barna Saha:
How Compression and Approximation Affect Efficiency in String Distance Measures. CoRR abs/2112.05836 (2021) - 2020
- [c5]Arun Ganesh, Bruce M. Maggs, Debmalya Panigrahi:
Robust Algorithms for TSP and Steiner Tree. ICALP 2020: 54:1-54:18 - [c4]Arun Ganesh, Kunal Talwar:
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC. NeurIPS 2020 - [c3]Arun Ganesh, Aaron Sy:
Near-Linear Time Edit Distance for Indel Channels. WABI 2020: 17:1-17:18 - [i6]Arun Ganesh, Bruce M. Maggs, Debmalya Panigrahi:
Robust Algorithms for TSP and Steiner Tree. CoRR abs/2005.08137 (2020) - [i5]Arun Ganesh, Aaron Sy:
Near-Linear Time Edit Distance for Indel Channels. CoRR abs/2007.03040 (2020) - [i4]Arun Ganesh, Jiazheng Zhao:
Privately Answering Counting Queries with Generalized Gaussian Mechanisms. CoRR abs/2010.01457 (2020) - [i3]Arun Ganesh, Kunal Talwar:
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC. CoRR abs/2010.14658 (2020)
2010 – 2019
- 2019
- [c2]Arun Ganesh, Qiuyi (Richard) Zhang:
Optimal sequence length requirements for phylogenetic tree reconstruction with indels. STOC 2019: 721-732 - 2018
- [i2]Arun Ganesh, Qiuyi (Richard) Zhang:
Optimal Sequence Length Requirements for Phylogenetic Tree Reconstruction with Indels. CoRR abs/1811.01121 (2018) - 2017
- [c1]Yossi Azar, Arun Ganesh, Rong Ge, Debmalya Panigrahi:
Online service with delay. STOC 2017: 551-563 - [i1]Yossi Azar, Arun Ganesh, Rong Ge, Debmalya Panigrahi:
Online Service with Delay. CoRR abs/1708.05611 (2017)
Coauthor Index
aka: Abhradeep Guha Thakurta
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