Open Problem: Is Margin Sufficient for Non-Interactive Private Distributed Learning?

Amit Daniely, Vitaly Feldman
Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:3180-3184, 2019.

Abstract

We ask whether every class of Boolean functions that has polynomial margin complexity can be PAC learned efficiently by a non-interactive locally differentially private algorithm.

Cite this Paper

BibTeX
@InProceedings{pmlr-v99-daniely19a, title = {Open Problem: Is Margin Sufficient for Non-Interactive Private Distributed Learning?}, author = {Daniely, Amit and Feldman, Vitaly}, booktitle = {Proceedings of the Thirty-Second Conference on Learning Theory}, pages = {3180--3184}, year = {2019}, editor = {Beygelzimer, Alina and Hsu, Daniel}, volume = {99}, series = {Proceedings of Machine Learning Research}, month = {25--28 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v99/daniely19a/daniely19a.pdf}, url = {https://proceedings.mlr.press/v99/daniely19a.html}, abstract = {We ask whether every class of Boolean functions that has polynomial margin complexity can be PAC learned efficiently by a non-interactive locally differentially private algorithm.} }
Endnote
%0 Conference Paper %T Open Problem: Is Margin Sufficient for Non-Interactive Private Distributed Learning? %A Amit Daniely %A Vitaly Feldman %B Proceedings of the Thirty-Second Conference on Learning Theory %C Proceedings of Machine Learning Research %D 2019 %E Alina Beygelzimer %E Daniel Hsu %F pmlr-v99-daniely19a %I PMLR %P 3180--3184 %U https://proceedings.mlr.press/v99/daniely19a.html %V 99 %X We ask whether every class of Boolean functions that has polynomial margin complexity can be PAC learned efficiently by a non-interactive locally differentially private algorithm.
APA
Daniely, A. & Feldman, V.. (2019). Open Problem: Is Margin Sufficient for Non-Interactive Private Distributed Learning?. Proceedings of the Thirty-Second Conference on Learning Theory, in Proceedings of Machine Learning Research 99:3180-3184 Available from https://proceedings.mlr.press/v99/daniely19a.html.

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