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Optimization and Learning With Randomly Compressed Gradient Updates
Zhanliang Huang
, Yunwen Lei
, Ata Kabán
Department of Mathematics
Research output
:
Contribution to journal
›
Journal article
›
peer-review
4
Citations (Scopus)
Overview
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Dive into the research topics of 'Optimization and Learning With Randomly Compressed Gradient Updates'. Together they form a unique fingerprint.
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Keyphrases
Gradient Update
100%
Stochastic Gradient Descent
66%
Almost Optimal
66%
Gradient Method
33%
Convergence Rate
33%
Stability Bound
33%
Optimization Algorithm
33%
Problem-based
33%
ProB
33%
Optimal Rate
33%
Differentially Private
33%
Efficient Optimization
33%
Uniform Stability
33%
Generalization Analysis
33%
Nonsmooth Problems
33%
Population Risk
33%
Private Settings
33%
Risk Bounds
33%
Mini-batch Gradient Descent
33%
Optimal Population Size
33%
Optimization Rate
33%
Mathematics
Stochastics
100%
Convergence Rate
50%
Widespread Application
50%