Articles with public access mandates - Uri StemmerLearn more
Available somewhere: 27
Black-box differential privacy for interactive ml
H Kaplan, Y Mansour, S Moran, K Nissim, U Stemmer
Advances in Neural Information Processing Systems 36, 2024
Mandates: European Commission
Concurrent shuffle differential privacy under continual observation
J Tenenbaum, H Kaplan, Y Mansour, U Stemmer
International Conference on Machine Learning, 33961-33982, 2023
Mandates: European Commission
On differential privacy and adaptive data analysis with bounded space
I Dinur, U Stemmer, DP Woodruff, S Zhou
Annual International Conference on the Theory and Applications of …, 2023
Mandates: US National Science Foundation, European Commission
Adversarially robust streaming algorithms via differential privacy
A Hassidim, H Kaplan, Y Mansour, Y Matias, U Stemmer
Journal of the ACM 69 (6), 1-14, 2022
Mandates: European Commission, Federal Ministry of Education and Research, Germany
Monotone learning
OJ Bousquet, A Daniely, H Kaplan, Y Mansour, S Moran, U Stemmer
Conference on Learning Theory, 842-866, 2022
Mandates: US National Science Foundation, European Commission
Friendlycore: Practical differentially private aggregation
E Tsfadia, E Cohen, H Kaplan, Y Mansour, U Stemmer
International Conference on Machine Learning, 21828-21863, 2022
Mandates: European Commission
Dynamic algorithms against an adaptive adversary: generic constructions and lower bounds
A Beimel, H Kaplan, Y Mansour, K Nissim, T Saranurak, U Stemmer
Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing …, 2022
Mandates: US National Science Foundation, European Commission
Differentially private multi-armed bandits in the shuffle model
J Tenenbaum, H Kaplan, Y Mansour, U Stemmer
Advances in Neural Information Processing Systems 34, 24956-24967, 2021
Mandates: European Commission, Federal Ministry of Education and Research, Germany
Separating adaptive streaming from oblivious streaming using the bounded storage model
H Kaplan, Y Mansour, K Nissim, U Stemmer
Annual International Cryptology Conference, 94-121, 2021
Mandates: US National Science Foundation, European Commission, Federal Ministry of …
The sparse vector technique, revisited
H Kaplan, Y Mansour, U Stemmer
Conference on Learning Theory, 2747-2776, 2021
Mandates: European Commission, Federal Ministry of Education and Research, Germany
Differentially-private clustering of easy instances
E Cohen, H Kaplan, Y Mansour, U Stemmer, E Tsfadia
International Conference on Machine Learning, 2049-2059, 2021
Mandates: European Commission
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity Full Version
H Kaplan, Y Mansour, U Stemmer, E Tsfadia
Mandates: European Commission, Federal Ministry of Education and Research, Germany
On the round complexity of the shuffle model
A Beimel, I Haitner, K Nissim, U Stemmer
Theory of Cryptography Conference, 683-712, 2020
Mandates: US National Science Foundation, European Commission
Closure properties for private classification and online prediction
N Alon, A Beimel, S Moran, U Stemmer
Conference on Learning Theory, 119-152, 2020
Mandates: US National Science Foundation, European Commission
Privately learning thresholds: Closing the exponential gap
H Kaplan, K Ligett, Y Mansour, M Naor, U Stemmer
Conference on Learning Theory, 2263-2285, 2020
Mandates: US National Science Foundation, US Department of Defense
Private k-means clustering with stability assumptions
M Shechner, O Sheffet, U Stemmer
International Conference on Artificial Intelligence and Statistics, 2518-2528, 2020
Mandates: Natural Sciences and Engineering Research Council of Canada
Private learning of halfspaces: Simplifying the construction and reducing the sample complexity
H Kaplan, Y Mansour, U Stemmer, E Tsfadia
Advances in Neural Information Processing Systems 33, 13976-13985, 2020
Mandates: European Commission, Federal Ministry of Education and Research, Germany
Heavy hitters and the structure of local privacy
M Bun, J Nelson, U Stemmer
ACM Transactions on Algorithms (TALG) 15 (4), 1-40, 2019
Mandates: US National Science Foundation, US Department of Defense
Private center points and learning of halfspaces
A Beimel, S Moran, K Nissim, U Stemmer
Conference on Learning Theory, 269-282, 2019
Mandates: US National Science Foundation
Characterizing the sample complexity of pure private learners
A Beimel, K Nissim, U Stemmer
Journal of Machine Learning Research 20 (146), 1-33, 2019
Mandates: US National Science Foundation
Publication and funding information is determined automatically by a computer program