| Differentially private learning with adaptive clipping G Andrew, O Thakkar, B McMahan, S Ramaswamy Advances in Neural Information Processing Systems 34, 17455-17466, 2021 | 294 | 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 | 225 | 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 | 201 | 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 | 120* | 2021 |
| Differentially private learning with adaptive clipping O Thakkar, G Andrew, HB McMahan arXiv e-prints, arXiv: 1905.03871, 2019 | 114 | 2019 |
| 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 | 97 | 2016 |
| 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 | 94 | 2022 |
| Model-Agnostic Private Learning R Bassily, O Thakkar, A Thakurta Neural Information Processing Systems 2018, 2018 | 92* | 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 | 89 | 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 | 84 | 2020 |
| 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 | 75* | 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 | 59 | 2021 |
| Differentially Private Matrix Completion, Revisited P Jain, O Thakkar, A Thakurta International Conference on Machine Learning 2018, 2018 | 46 | 2018 |
| 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 | 43 | 2023 |
| 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 | 39 | 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 | 35 | 2021 |
| 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 | 16 | 2020 |
| Detecting unintended memorization in language-model-fused ASR WR Huang, S Chien, O Thakkar, R Mathews arXiv preprint arXiv:2204.09606, 2022 | 12 | 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 | 11 | 2022 |
| Unintended memorization in large asr models, and how to mitigate it L Wang, O Thakkar, R Mathews ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024 | 8 | 2024 |
Abhradeep Guha ThakurtaGoogle Research - Brain TeamVerified email at google.com