| Differentially private learning with adaptive clipping G Andrew, O Thakkar, B McMahan, S Ramaswamy Advances in Neural Information Processing Systems 34, 17455-17466, 2021 | 317 | 2021 |
| Towards Practical Differentially Private Convex Optimization R Iyengar, JP Near, D Song, O Thakkar, A Thakurta, L Wang IEEE Symposium on Security and Privacy 2019, 2019 | 196 | 2019 |
| Practical and private (deep) learning without sampling or shuffling P Kairouz, B McMahan, S Song, O Thakkar, A Thakurta, Z Xu International Conference on Machine Learning 2021, 5213-5225, 2021 | 148 | 2021 |
| Evading the curse of dimensionality in unconstrained private glms S Song, T Steinke, O Thakkar, A Thakurta International Conference on Artificial Intelligence and Statistics, 2638-2646, 2021 | 100* | 2021 |
| Max-information, differential privacy, and post-selection hypothesis testing R Rogers, A Roth, A Smith, O Thakkar IEEE Symposium on Foundations of Computer Science 2016, 2016 | 89 | 2016 |
| Model-Agnostic Private Learning R Bassily, O Thakkar, A Thakurta Neural Information Processing Systems 2018, 2018 | 86* | 2018 |
| Privacy amplification via random check-ins B Balle, P Kairouz, B McMahan, O Thakkar, A Guha Thakurta Advances in Neural Information Processing Systems 33, 4623-4634, 2020 | 73 | 2020 |
| Training production language models without memorizing user data S Ramaswamy, O Thakkar, R Mathews, G Andrew, HB McMahan, ... arXiv preprint arXiv:2009.10031, 2020 | 71 | 2020 |
| Measuring forgetting of memorized training examples M Jagielski, O Thakkar, F Tramer, D Ippolito, K Lee, N Carlini, E Wallace, ... arXiv preprint arXiv:2207.00099, 2022 | 65 | 2022 |
| Understanding unintended memorization in language models under federated learning OD Thakkar, S Ramaswamy, R Mathews, F Beaufays Proceedings of the Third Workshop on Privacy in Natural Language Processing …, 2021 | 56* | 2021 |
| 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 2022, 2021 | 50 | 2021 |
| Differentially Private Matrix Completion, Revisited P Jain, O Thakkar, A Thakurta International Conference on Machine Learning 2018, 2018 | 43 | 2018 |
| Revealing and protecting labels in distributed training T Dang, O Thakkar, S Ramaswamy, R Mathews, P Chin, F Beaufays Advances in Neural Information Processing Systems 34, 1727-1738, 2021 | 33 | 2021 |
| The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection S Mohapatra, S Sasy, X He, G Kamath, O Thakkar 36th AAAI Conference on Artificial Intelligence, 2021 | 33 | 2021 |
| 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 | 29 | 2023 |
| Guaranteed validity for empirical approaches to adaptive data analysis R Rogers, A Roth, A Smith, N Srebro, O Thakkar, B Woodworth International Conference on Artificial Intelligence and Statistics, 2830-2840, 2020 | 11 | 2020 |
| Detecting unintended memorization in language-model-fused ASR WR Huang, S Chien, O Thakkar, R Mathews arXiv preprint arXiv:2204.09606, 2022 | 10 | 2022 |
| A method to reveal speaker identity in distributed asr training, and how to counter it T Dang, O Thakkar, S Ramaswamy, R Mathews, P Chin, F Beaufays ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 7 | 2022 |
| 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 |
| Extracting targeted training data from ASR models, and how to mitigate it E Amid, O Thakkar, A Narayanan, R Mathews, F Beaufays arXiv preprint arXiv:2204.08345, 2022 | 5 | 2022 |
Abhradeep Guha ThakurtaGoogle Research - Brain Team在 google.com 的电子邮件经过验证
