Sample-efficient proper PAC learning with approximate differential privacy-Reference-Cited by-同舟云学术

Sample-efficient proper PAC learning with approximate differential privacy

Published:2021-06-15 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing
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Author:

Ghazi Badih¹,Golowich Noah²,Kumar Ravi¹,Manurangsi Pasin¹

Affiliation:

1. Google Research, USA

2. Massachusetts Institute of Technology, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3406325.3451028

Reference55 articles.

1. Naman Agarwal and Karan Singh. 2017. The Price of Differential Privacy for Online Learning. In ICML. Pages 32–40. Naman Agarwal and Karan Singh. 2017. The Price of Differential Privacy for Online Learning. In ICML. Pages 32–40.

2. Noga Alon Amos Beimel Shay Moran and Uri Stemmer. 2020. Closure Properties for Private Classification and Online Prediction. In COLT. Pages 119–152. Noga Alon Amos Beimel Shay Moran and Uri Stemmer. 2020. Closure Properties for Private Classification and Online Prediction. In COLT. Pages 119–152.

3. Noga Alon Omri Ben-Eliezer Yuval Dagan Shay Moran Moni Naor and Eylon Yogev. 2021. Adversarial Laws of Large Numbers and Optimal Regret in Online Classification. In STOC. Noga Alon Omri Ben-Eliezer Yuval Dagan Shay Moran Moni Naor and Eylon Yogev. 2021. Adversarial Laws of Large Numbers and Optimal Regret in Online Classification. In STOC.

4. Noga Alon Roi Livni Maryanthe Malliaris and Shay Moran. 2019. Private PAC Learning Implies Finite Littlestone Dimension. In STOC. Pages 852–860. Noga Alon Roi Livni Maryanthe Malliaris and Shay Moran. 2019. Private PAC Learning Implies Finite Littlestone Dimension. In STOC. Pages 852–860.

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