Skip to main navigation
Skip to search
Skip to main content
Experts@Minnesota Home
Home
Profiles
Research units
University Assets
Projects and Grants
Research output
Datasets
Press/Media
Activities
Fellowships, Honors, and Prizes
Impacts
Search by expertise, name or affiliation
Understanding gradient clipping in private SGD: A geometric perspective
Xiangyi Chen
,
Steven Wu
,
Mingyi Hong
Computer Science and Engineering
Electrical and Computer Engineering
Research output
:
Contribution to journal
›
Conference article
›
peer-review
140
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Understanding gradient clipping in private SGD: A geometric perspective'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Asymmetric Gradient
25%
Deep Learning Model
25%
Differential Privacy
25%
Differentially Private
25%
Disparity Index
25%
Geometric Perspective
100%
Gradient Clipping
100%
Gradient Distribution
75%
L2 Norm
25%
Learning Systems
25%
Machine Learning Applications
25%
Popular
25%
Privacy Guarantees
25%
Stationary Point
25%
Symmetric Distribution
25%
Symmetric Structure
25%
Training Data
25%
Mathematics
Asymmetric
100%
Deep Learning Method
100%
Stationary Point
100%
Symmetric Distribution
100%
Training Data
100%
Computer Science
Deep Learning Model
50%
Differential Privacy
50%
Learning System
100%
Machine Learning
50%
Sensitive Informations
50%
Stationary Point
50%
Training Data
50%