 Home
 Search Results
 Page 1 of 1
Search for: All records

Total Resources1
 Resource Type

00000010000
 More
 Availability

10
 Author / Contributor
 Filter by Author / Creator


Alon, N. (1)

Bassily, R. (1)

and Moran, S. (1)

#Tyler Phillips, Kenneth E. (0)

#Willis, Ciara (0)

& AbreuRamos, E. D. (0)

& Abramson, C. I. (0)

& AbreuRamos, E. D. (0)

& Adams, S.G. (0)

& Ahmed, K. (0)

& Ahmed, Khadija. (0)

& Aina, D.K. Jr. (0)

& AkcilOkan, O. (0)

& Akuom, D. (0)

& Aleven, V. (0)

& AndrewsLarson, C. (0)

& Archibald, J. (0)

& Arnett, N. (0)

& Arya, G. (0)

& Attari, S. Z. (0)

 Filter by Editor


& Spizer, S. M. (0)

& . Spizer, S. (0)

& Ahn, J. (0)

& Bateiha, S. (0)

& Bosch, N. (0)

& Brennan K. (0)

& Brennan, K. (0)

& Chen, B. (0)

& Chen, Bodong (0)

& Drown, S. (0)

& Ferretti, F. (0)

& Higgins, A. (0)

& J. Peters (0)

& Kali, Y. (0)

& RuizArias, P.M. (0)

& S. Spitzer (0)

& Sahin. I. (0)

& Spitzer, S. (0)

& Spitzer, S.M. (0)

(submitted  in Review for IEEE ICASSP2024) (0)


Have feedback or suggestions for a way to improve these results?
!
Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to nonfederal websites. Their policies may differ from this site.

We consider learning problems where the training set consists of two types of examples: private and public. The goal is to design a learning algorithm that satisfies differential privacy only with respect to the private examples. This setting interpolates between private learning (where private) and classical learning (where all examples are public). We study the limits of learning in this setting in terms of private and public sample complexities. We show that any hypothesis class of VCdimension d can be agnostically learned up to an excess error of α using only (roughly) d/α public examples and d/α2 private labeled examples. This result holds even when the public examples are unlabeled. This gives a quadratic improvement over the standard d/α2 upper bound on the public sample complexity (where private examples can be ignored altogether if the public examples are labeled). Furthermore, we give a nearly matching lower bound, which we prove via a generic reduction from this setting to the one of private learning without public data.more » « less