| The johnson-lindenstrauss transform itself preserves differential privacy J Blocki, A Blum, A Datta, O Sheffet 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science, 410-419, 2012 | 287 | 2012 |
| Differentially private data analysis of social networks via restricted sensitivity J Blocki, A Blum, A Datta, O Sheffet Proceedings of the 4th conference on Innovations in Theoretical Computer …, 2013 | 260 | 2013 |
| Optimal social choice functions: A utilitarian view C Boutilier, I Caragiannis, S Haber, T Lu, AD Procaccia, O Sheffet Proceedings of the 13th ACM Conference on Electronic Commerce, 197-214, 2012 | 254 | 2012 |
| Center-based clustering under perturbation stability P Awasthi, A Blum, O Sheffet Information Processing Letters 112 (1-2), 49-54, 2012 | 168 | 2012 |
| Differentially private ordinary least squares O Sheffet International Conference on Machine Learning, 3105-3114, 2017 | 151 | 2017 |
| Improved spectral-norm bounds for clustering P Awasthi, O Sheffet International Workshop on Approximation Algorithms for Combinatorial …, 2012 | 131 | 2012 |
| Differentially private contextual linear bandits R Shariff, O Sheffet Advances in Neural Information Processing Systems 31, 2018 | 122 | 2018 |
| Stability yields a PTAS for k-median and k-means clustering P Awasthi, A Blum, O Sheffet 2010 IEEE 51st Annual Symposium on Foundations of Computer Science, 309-318, 2010 | 111 | 2010 |
| Send mixed signals: earn more, work less P Bro Miltersen, O Sheffet Proceedings of the 13th ACM conference on electronic commerce, 234-247, 2012 | 104 | 2012 |
| Learning mixtures of ranking models P Awasthi, A Blum, O Sheffet, A Vijayaraghavan Advances in Neural Information Processing Systems 27, 2014 | 90 | 2014 |
| Old techniques in differentially private linear regression O Sheffet Algorithmic Learning Theory, 789-827, 2019 | 66* | 2019 |
| Locally Private Mean Estimation: -test and Tight Confidence Intervals M Gaboardi, R Rogers, O Sheffet The 22nd international conference on artificial intelligence and statistics …, 2019 | 64 | 2019 |
| Locally private hypothesis testing O Sheffet International Conference on Machine Learning, 4605-4614, 2018 | 62 | 2018 |
| Differentially private algorithms for learning mixtures of separated gaussians G Kamath, O Sheffet, V Singhal, J Ullman Advances in Neural Information Processing Systems 32, 2019 | 59 | 2019 |
| An optimal private stochastic-mab algorithm based on optimal private stopping rule T Sajed, O Sheffet International Conference on Machine Learning, 5579-5588, 2019 | 51 | 2019 |
| Optimizing password composition policies J Blocki, S Komanduri, A Procaccia, O Sheffet Proceedings of the fourteenth ACM conference on Electronic commerce, 105-122, 2013 | 47 | 2013 |
| Graph colouring with no large monochromatic components N Linial, J Matoušek, O Sheffet, G Tardos Combinatorics, Probability and Computing 17 (4), 577-589, 2008 | 46 | 2008 |
| Systems and methods for video monitoring using linked devices I Horovitz, S Kiro, O Sheffet US Patent 8,531,522, 2013 | 37 | 2013 |
| On the randomness complexity of property testing O Goldreich, O Sheffet Computational Complexity 19, 99-133, 2010 | 26 | 2010 |
| The power of synergy in differential privacy: Combining a small curator with local randomizers A Beimel, A Korolova, K Nissim, O Sheffet, U Stemmer arXiv preprint arXiv:1912.08951, 2019 | 20 | 2019 |
Avrim BlumProfessor and Chief Academic Officer, Toyota Technological Institute at ChicagoVerified email at ttic.edu
