Articles with public access mandates - Uri StemmerLearn more
Available somewhere: 27
Algorithmic stability for adaptive data analysis
R Bassily, K Nissim, A Smith, T Steinke, U Stemmer, J Ullman
Proceedings of the forty-eighth annual ACM symposium on Theory of Computing …, 2016
Mandates: US National Science Foundation
Practical locally private heavy hitters
R Bassily, K Nissim, U Stemmer, A Guha Thakurta
Advances in Neural Information Processing Systems 30, 2017
Mandates: US National Science Foundation
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
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
Simultaneous private learning of multiple concepts
M Bun, K Nissim, U Stemmer
Journal of Machine Learning Research 20 (94), 1-34, 2019
Mandates: US National Science Foundation, US Department of Defense
Clustering algorithms for the centralized and local models
K Nissim, U Stemmer
Algorithmic Learning Theory, 619-653, 2018
Mandates: US National Science Foundation
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
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
Locating a small cluster privately
K Nissim, U Stemmer, S Vadhan
Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2016
Mandates: US National Science Foundation
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
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 …
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
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
Learning privately with labeled and unlabeled examples
A Beimel, K Nissim, U Stemmer
Proceedings of the twenty-sixth annual ACM-SIAM symposium on Discrete …, 2014
Mandates: 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
The limits of post-selection generalization
J Ullman, A Smith, K Nissim, U Stemmer, T Steinke
Advances in Neural Information Processing Systems 31, 2018
Mandates: US National Science Foundation
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
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
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
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
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