Optimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization-Reference-Cited by-同舟云学术

Optimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization

Published:2023-06-02 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 55th Annual ACM Symposium on Theory of Computing
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Author:

Cohen Edith¹,Lyu Xin²,Nelson Jelani²,Sarlós Tamás³,Stemmer Uri⁴

Affiliation:

1. Google Research, USA / Tel Aviv University, Israel

2. University of California at Berkeley, Berkeley, USA / Google Research, USA

3. Google Research, USA

4. Tel Aviv University, Israel / Google Research, Israel

Funder

Israel Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3564246.3585148

Reference24 articles.

1. Private and Online Learnability Are Equivalent

2. Noga Alon Roi Livni Maryanthe Malliaris and Shay Moran. 2019. Private PAC learning implies finite Littlestone dimension. In STOC. Noga Alon Roi Livni Maryanthe Malliaris and Shay Moran. 2019. Private PAC learning implies finite Littlestone dimension. In STOC.

3. Borja Balle Gilles Barthe and Marco Gaboardi. 2018. Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences. In NeurIPS. 6280–6290. Borja Balle Gilles Barthe and Marco Gaboardi. 2018. Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences. In NeurIPS. 6280–6290.

4. Amos Beimel , Shiva Prasad Kasiviswanathan, and Kobbi Nissim . 2010 . Bounds on the Sample Complexity for Private Learning and Private Data Release. In TCC. Amos Beimel, Shiva Prasad Kasiviswanathan, and Kobbi Nissim. 2010. Bounds on the Sample Complexity for Private Learning and Private Data Release. In TCC.

5. Amos Beimel Shay Moran Kobbi Nissim and Uri Stemmer. 2019. Private Center Points and Learning of Halfspaces. In COLT. Amos Beimel Shay Moran Kobbi Nissim and Uri Stemmer. 2019. Private Center Points and Learning of Halfspaces. In COLT.