Uri Stemmer

Ben-Gurion University, Beer Sheva, Israel

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Papers20 papers

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Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity

Kaplan Haim,Mansour Yishay,Stemmer Uri, Tsfadia Eliad

Bibtex

Practical Locally Private Heavy Hitters

Raef Bassily,Kobbi Nissim,Uri Stemmer,Abhradeep Thakurta

Journal of Machine Learning Research, no. 16 (2020): 1-42

Bibtex

Private Center Points and Learning of Halfspaces.

Amos Beimel,Shay Moran,Kobbi Nissim,Uri Stemmer

COLT, (2019): 269-282

EIBibtex

Characterizing the Sample Complexity of Pure Private Learners

Amos Beimel,Kobbi Nissim,Uri Stemmer

Journal of Machine Learning Research, no. 146 (2019): 1-33

Bibtex

Simultaneous Private Learning of Multiple Concepts

Mark Bun,Kobbi Nissim,Uri Stemmer

Journal of Machine Learning Research, no. 94 (2019)

Cited by18EIBibtex

Heavy Hitters and the Structure of Local Privacy

Mark Bun,Jelani Nelson,Uri Stemmer

SIGMOD/PODS '18: International Conference on Management of Data Houston TX U..., no. 4 (2019): 1-40

Cited by37EIBibtex

Differentially Private Learning of Geometric Concepts.

Haim Kaplan,Yishay Mansour,Yossi Matias,Uri Stemmer

International Conference on Machine Learning, (2019): 3233-3241

EIBibtex

Concentration Bounds for High Sensitivity Functions Through Differential Privacy

Uri Stemmer,Kobbi Nissim

Journal of Privacy and Confidentiality, no. 1 (2019)

Bibtex

Clustering Algorithms for the Centralized and Local Models.

Kobbi Nissim,Uri Stemmer

algorithmic learning theory, (2018)

Cited by8EIBibtex

Differentially Private k-Means with Constant Multiplicative Error.

Haim Kaplan,Uri Stemmer

CoRR, (2018)

EIBibtex

The Limits of Post-Selection Generalization.

Kobbi Nissim,Adam D. Smith,Thomas Steinke,Uri Stemmer,Jonathan Ullman

CoRR, (2018)

EIBibtex

The Limits of Post-Selection Generalization.

Jonathan Ullman,Adam D. Smith,Kobbi Nissim,Uri Stemmer,Thomas Steinke

NeurIPS, pp.6402-6411, (2018)

Cited by1EIBibtex

Differentially Private k-Means with Constant Multiplicative Error.

Uri Stemmer,Haim Kaplan

NeurIPS, pp.5436-5446, (2018)

EIBibtex

Practical Locally Private Heavy Hitters.

Raef Bassily,Kobbi Nissim,Uri Stemmer,Abhradeep Thakurta

NIPS, (2017): 2288-2296

Cited by35EIBibtex

Concentration Bounds for High Sensitivity Functions Through Differential Privacy.

Kobbi Nissim,Uri Stemmer

arXiv: Learning, (2017)

Cited by3EIBibtex

Locating a Small Cluster Privately.

Kobbi Nissim,Uri Stemmer,Salil P. Vadhan

PODS, (2016)

Cited by10EIBibtex

Algorithmic Stability for Adaptive Data Analysis

Raef Bassily,Kobbi Nissim,Adam Smith,Thomas Steinke,Uri Stemmer,Jonathan Ullman

STOC '16: Symposium on Theory of Computing Cambridge MA USA June, 2016, (2016): 1046-1059

Cited by128EIBibtex

Private Learning and Sanitization: Pure vs. Approximate Differential Privacy.

Amos Beimel,Kobbi Nissim,Uri Stemmer

Theory of Computing, no. 1 (2016)

Cited by44EIBibtex

Learning privately with labeled and unlabeled examples

Amos Beimel,Kobbi Nissim,Uri Stemmer

SODA, (2015): 461-477

Cited by10EIBibtex

Characterizing the sample complexity of private learners

Amos Beimel,Kobbi Nissim,Uri Stemmer

Proceedings of the 4th conference on Innovations in Theoretical Computer Science, (2013): 97-110

Cited by32EIBibtex

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