Private PAC learning implies finite Littlestone dimension-Reference-Cited by-同舟云学术

Private PAC learning implies finite Littlestone dimension

Published:2019-06-23 Issue: Volume: Page:
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Container-title:Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing
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Author:

Alon Noga¹,Livni Roi²,Malliaris Maryanthe³,Moran Shay⁴

Affiliation:

1. Princeton University, USA / Tel Aviv University, Israel

2. Tel Aviv University, Israel

3. University of Chicago, USA

4. Princeton University, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3313276.3316312

Reference53 articles.

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2. Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy. Algorithmica 72 1 (2015) 282–315. 10.1007/s00453-014-9954-9 Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy. Algorithmica 72 1 (2015) 282–315. 10.1007/s00453-014-9954-9

3. Algorithmic stability for adaptive data analysis

4. Raef Bassily Om Thakkar and Abhradeep Thakurta. 2018. Raef Bassily Om Thakkar and Abhradeep Thakurta. 2018.

5. Model-Agnostic Private Learning via Stability. CoRR abs/1803.05101 (2018). Model-Agnostic Private Learning via Stability. CoRR abs/1803.05101 (2018).

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