| 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 | 478 | 2019 |
| Does learning require memorization? a short tale about a long tail V Feldman Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing …, 2020 | 455 | 2020 |
| Preserving statistical validity in adaptive data analysis C Dwork, V Feldman, M Hardt, T Pitassi, O Reingold, AL Roth Proceedings of the forty-seventh annual ACM symposium on Theory of computing …, 2015 | 413 | 2015 |
| What neural networks memorize and why: Discovering the long tail via influence estimation V Feldman, C Zhang Advances in Neural Information Processing Systems 33, 2881-2891, 2020 | 409 | 2020 |
| The reusable holdout: Preserving validity in adaptive data analysis C Dwork, V Feldman, M Hardt, T Pitassi, O Reingold, A Roth Science 349 (6248), 636-638, 2015 | 397 | 2015 |
| Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores AS Cassidy, P Merolla, JV Arthur, SK Esser, B Jackson, R Alvarez-Icaza, ... The 2013 international joint conference on neural networks (IJCNN), 1-10, 2013 | 355 | 2013 |
| Statistical algorithms and a lower bound for detecting planted cliques V Feldman, E Grigorescu, L Reyzin, S Vempala, Y Xiao Journal of the ACM (JACM), 2017, 655-664, 2013 | 292* | 2013 |
| On agnostic learning of parities, monomials, and halfspaces V Feldman, P Gopalan, S Khot, AK Ponnuswami SIAM J. Comput 39 (2), 606-645, 2009 | 290* | 2009 |
| Generalization in adaptive data analysis and holdout reuse C Dwork, V Feldman, M Hardt, T Pitassi, O Reingold, A Roth Advances in neural information processing systems 28, 2015 | 260 | 2015 |
| Private stochastic convex optimization with optimal rates R Bassily, V Feldman, K Talwar, A Guha Thakurta Advances in neural information processing systems 32, 2019 | 249 | 2019 |
| Stability of stochastic gradient descent on nonsmooth convex losses R Bassily, V Feldman, C Guzmán, K Talwar Advances in Neural Information Processing Systems 33, 4381-4391, 2020 | 189 | 2020 |
| Private stochastic convex optimization: optimal rates in linear time V Feldman, T Koren, K Talwar Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing …, 2020 | 188 | 2020 |
| Privacy amplification by iteration V Feldman, I Mironov, K Talwar, A Thakurta 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS …, 2018 | 185 | 2018 |
| Agnostic learning of monomials by halfspaces is hard V Feldman, V Guruswami, P Raghavendra, Y Wu SIAM Journal on Computing 41 (6), 1558-1590, 2012 | 177 | 2012 |
| High probability generalization bounds for uniformly stable algorithms with nearly optimal rate V Feldman, J Vondrak Conference on Learning Theory, 1270-1279, 2019 | 160 | 2019 |
| On the complexity of random satisfiability problems with planted solutions V Feldman, W Perkins, S Vempala Proceedings of the forty-seventh annual ACM symposium on Theory of Computing …, 2015 | 150 | 2015 |
| Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling V Feldman, A McMillan, K Talwar 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS …, 2022 | 143 | 2022 |
| Statistical query algorithms for mean vector estimation and stochastic convex optimization V Feldman, C Guzman, S Vempala Mathematics of Operations Research 46 (3), 912-945, 2021 | 113* | 2021 |
| The complexity of properly learning simple concept classes M Alekhnovich, M Braverman, V Feldman, AR Klivans, T Pitassi Journal of Computer and System Sciences 74 (1), 16-34, 2008 | 107* | 2008 |
| Privacy-preserving prediction C Dwork, V Feldman Conference On Learning Theory, 1693-1702, 2018 | 106 | 2018 |
Moritz HardtMax Planck Institute for Intelligent SystemsVerified email at is.mpg.de
