| Private empirical risk minimization: Efficient algorithms and tight error bounds R Bassily, A Smith, A Thakurta 2014 IEEE 55th Annual Symposium on Foundations of Computer Science, 464-473, 2014 | 405 | 2014 |
| Discovering frequent patterns in sensitive data R Bhaskar, S Laxman, A Smith, A Thakurta Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010 | 261 | 2010 |
| Private convex empirical risk minimization and high-dimensional regression D Kifer, A Smith, A Thakurta Conference on Learning Theory, 25.1-25.40, 2012 | 258 | 2012 |
| GUPT: privacy preserving data analysis made easy P Mohan, A Thakurta, E Shi, D Song, D Culler Proceedings of the 2012 ACM SIGMOD International Conference on Management of …, 2012 | 253 | 2012 |
| Differentially private online learning P Jain, P Kothari, A Thakurta Conference on Learning Theory, 24.1-24.34, 2012 | 182 | 2012 |
| Analyze gauss: optimal bounds for privacy-preserving principal component analysis C Dwork, K Talwar, A Thakurta, L Zhang Proceedings of the forty-sixth annual ACM symposium on Theory of computing …, 2014 | 176 | 2014 |
| Practical locally private heavy hitters R Bassily, K Nissim, U Stemmer, A Thakurta arXiv preprint arXiv:1707.04982, 2017 | 130 | 2017 |
| Differentially private feature selection via stability arguments, and the robustness of the lasso AG Thakurta, A Smith Conference on Learning Theory, 819-850, 2013 | 124 | 2013 |
| Amplification by shuffling: From local to central differential privacy via anonymity Ú Erlingsson, V Feldman, I Mironov, A Raghunathan, K Talwar, ... Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete …, 2019 | 105 | 2019 |
| Is interaction necessary for distributed private learning? A Smith, A Thakurta, J Upadhyay 2017 IEEE Symposium on Security and Privacy (SP), 58-77, 2017 | 102 | 2017 |
| Differentially private learning with kernels P Jain, A Thakurta International conference on machine learning, 118-126, 2013 | 93 | 2013 |
| Evaluating COVID-19 public health messaging in Italy: Self-reported compliance and growing mental health concerns S Barari, S Caria, A Davola, P Falco, T Fetzer, S Fiorin, L Hensel, ... MedRxiv, 2020 | 88 | 2020 |
| Nearly-optimal private LASSO K Talwar, A Thakurta, L Zhang Proceedings of the 28th International Conference on Neural Information …, 2015 | 82 | 2015 |
| Noiseless database privacy R Bhaskar, A Bhowmick, V Goyal, S Laxman, A Thakurta International Conference on the Theory and Application of Cryptology and …, 2011 | 73 | 2011 |
| Privacy amplification by iteration V Feldman, I Mironov, K Talwar, A Thakurta 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS …, 2018 | 52 | 2018 |
| Towards practical differentially private convex optimization R Iyengar, JP Near, D Song, O Thakkar, A Thakurta, L Wang 2019 IEEE Symposium on Security and Privacy (SP), 299-316, 2019 | 47 | 2019 |
| Private empirical risk minimization, revisited R Bassily, A Smith, A Thakurta rem 3, 19, 2014 | 42 | 2014 |
| Learning new words AG Thakurta, AH Vyrros, US Vaishampayan, G Kapoor, J Freudiger, ... US Patent 9,594,741, 2017 | 36 | 2017 |
| Emoji frequency detection and deep link frequency AG Thakurta, AH Vyrros, US Vaishampayan, G Kapoor, J Freudinger, ... US Patent 9,705,908, 2017 | 33 | 2017 |
| Private empirical risk minimization beyond the worst case: The effect of the constraint set geometry K Talwar, A Thakurta, L Zhang arXiv preprint arXiv:1411.5417, 2014 | 32 | 2014 |
Adam SmithProfessor of Computer Science, Boston UniversityVerified email at bu.edu
