| Federated Asymptotics: a model to compare federated learning algorithms G Cheng, K Chadha, J Duchi International Conference on Artificial Intelligence and Statistics, 10650-10689, 2023 | 50* | 2023 |
| A reinforcement learning algorithm for restless bandits VS Borkar, K Chadha 2018 Indian Control Conference (ICC), 89-94, 2018 | 18 | 2018 |
| Accelerated, optimal and parallel: Some results on model-based stochastic optimization K Chadha, G Cheng, J Duchi International Conference on Machine Learning, 2811-2827, 2022 | 14 | 2022 |
| Minibatch stochastic approximate proximal point methods H Asi, K Chadha, G Cheng, JC Duchi Advances in neural information processing systems 33, 21958-21968, 2020 | 14 | 2020 |
| Efficiency fairness tradeoff in battery sharing KN Chadha, AA Kulkarni, J Nair Operations Research Letters 49 (3), 377-384, 2021 | 4 | 2021 |
| Differentially private heavy hitter detection using federated analytics K Chadha, J Chen, J Duchi, V Feldman, H Hashemi, O Javidbakht, ... 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 512-533, 2024 | 3 | 2024 |
| Private optimization in the interpolation regime: faster rates and hardness results H Asi, K Chadha, G Cheng, J Duchi International Conference on Machine Learning, 1025-1045, 2022 | 3 | 2022 |
| Aggregate play and welfare in strategic interactions on networks KN Chadha, AA Kulkarni Journal of Mathematical Economics 88, 72-86, 2020 | 3 | 2020 |
| On independent cliques and linear complementarity problems KN Chadha, AA Kulkarni Indian Journal of Pure and Applied Mathematics 53 (4), 1036-1057, 2022 | 2* | 2022 |
| Private confidence sets K Chadha, J Duchi, R Kuditipudi NeurIPS 2021 Workshop Privacy in Machine Learning, 2021 | 2 | 2021 |
| Auditing Private Prediction K Chadha, M Jagielski, N Papernot, C Choquette-Choo, M Nasr arXiv preprint arXiv:2402.09403, 2024 | | 2024 |
| Resampling methods for Private Statistical Inference K Chadha, J Duchi, R Kuditipudi arXiv preprint arXiv:2402.07131, 2024 | | 2024 |
| 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)| 979-8-3503-4950-4/24/$31.00© 2024 IEEE| DOI: 10.1109/SaTML59370. 2024.00043 U Aïvodji, G Anderson, R Anderson, S Aydore, A Azize, D Basu, ... | | |
John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford UniversityDirección de correo verificada de stanford.edu
