Skip to main navigation
Skip to search
Skip to main content
KAUST PORTAL FOR RESEARCHERS AND STUDENTS Home
Home
Profiles
Research units
Research output
Press/Media
Prizes
Courses
Equipment
Student theses
Datasets
Search by expertise, name or affiliation
Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations
Di Wang
, Adam Smith, Jinhui Xu
Computer Science
Research output
:
Contribution to conference
›
Paper
›
peer-review
12
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Mathematics
Linear Models
100%
Polynomial Approximation
100%
Data Point
100%
Polynomial
50%
Inner Product
50%
Error Bound
50%
Loss Function
50%
Risk Function
50%
Median
50%
Excess Risk
50%
Euclidean Distance
50%
Keyphrases
Private Learning
100%
Euclidean Median
66%
Untrusted Aggregator
33%