| Public data-assisted mirror descent for private model training E Amid, A Ganesh, R Mathews, S Ramaswamy, S Song, T Steinke, ... International Conference on Machine Learning, 517-535, 2022 | 46 | 2022 |
| Online service with delay Y Azar, A Ganesh, R Ge, D Panigrahi Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing …, 2017 | 44 | 2017 |
| Faster differentially private samplers via Rényi divergence analysis of discretized Langevin MCMC A Ganesh, K Talwar Advances in Neural Information Processing Systems 33, 7222-7233, 2020 | 37 | 2020 |
| Why is public pretraining necessary for private model training? A Ganesh, M Haghifam, M Nasr, S Oh, T Steinke, O Thakkar, AG Thakurta, ... International Conference on Machine Learning, 10611-10627, 2023 | 26 | 2023 |
| Langevin diffusion: An almost universal algorithm for private euclidean (convex) optimization A Ganesh, A Thakurta, J Upadhyay arXiv preprint arXiv:2204.01585, 2022 | 16 | 2022 |
| (Amplified) Banded Matrix Factorization: A unified approach to private training CA Choquette-Choo, A Ganesh, R McKenna, HB McMahan, J Rush, ... Advances in Neural Information Processing Systems 36, 2024 | 11 | 2024 |
| How compression and approximation affect efficiency in string distance measures A Ganesh, T Kociumaka, A Lincoln, B Saha Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms …, 2022 | 11 | 2022 |
| Privately answering counting queries with generalized gaussian mechanisms A Ganesh, J Zhao arXiv preprint arXiv:2010.01457, 2020 | 10 | 2020 |
| Near-linear time edit distance for indel channels A Ganesh, A Sy arXiv preprint arXiv:2007.03040, 2020 | 10 | 2020 |
| Optimal sequence length requirements for phylogenetic tree reconstruction with indels A Ganesh, Q Zhang Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing …, 2019 | 9 | 2019 |
| Online service with delay Y Azar, A Ganesh, R Ge, D Panigrahi ACM Transactions on Algorithms (TALG) 17 (3), 1-31, 2021 | 8 | 2021 |
| Recycling scraps: Improving private learning by leveraging intermediate checkpoints V Shejwalkar, A Ganesh, R Mathews, O Thakkar, A Thakurta arXiv preprint arXiv:2210.01864, 2022 | 6 | 2022 |
| Faster differentially private convex optimization via second-order methods A Ganesh, M Haghifam, T Steinke, A Guha Thakurta Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
| Universality of langevin diffusion for private optimization, with applications to sampling from rashomon sets A Ganesh, A Thakurta, J Upadhyay The Thirty Sixth Annual Conference on Learning Theory, 1730-1773, 2023 | 5 | 2023 |
| Private (stochastic) non-convex optimization revisited: Second-order stationary points and excess risks A Ganesh, D Liu, S Oh, A Thakurta arXiv preprint arXiv:2302.09699, 2023 | 5 | 2023 |
| Correlated noise provably beats independent noise for differentially private learning CA Choquette-Choo, K Dvijotham, K Pillutla, A Ganesh, T Steinke, ... arXiv preprint arXiv:2310.06771, 2023 | 4 | 2023 |
| Universal algorithms for clustering problems A Ganesh, BM Maggs, D Panigrahi ACM Transactions on Algorithms 19 (2), 1-46, 2023 | 4 | 2023 |
| Robust algorithms for TSP and Steiner tree A Ganesh, BM Maggs, D Panigrahi ACM Transactions on Algorithms 19 (2), 1-37, 2023 | 4 | 2023 |
| Privacy Amplification for Matrix Mechanisms CA Choquette-Choo, A Ganesh, T Steinke, A Thakurta arXiv preprint arXiv:2310.15526, 2023 | 2 | 2023 |
| Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks D Liu, A Ganesh, S Oh, A Guha Thakurta Advances in Neural Information Processing Systems 36, 2024 | 1 | 2024 |
Abhradeep Guha ThakurtaGoogle Research - Brain TeamVerified email at google.com
