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Optimal Stochastic and Online Learning with Individual Iterates
Yunwen Lei
, Peng Yang
, Ke Tang
*
, Ding Xuan Zhou
*
Corresponding author for this work
Department of Mathematics
Research output
:
Chapter in book/report/conference proceeding
›
Conference proceeding
›
peer-review
4
Citations (Scopus)
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Keyphrases
Sparsity
100%
Training Intensity
100%
Online Learning
100%
Mirror Descent
100%
Composite Mirrors
100%
Stochastic Learning
100%
Stochastic Composite
100%
Machine Learning
50%
Nonsmooth
50%
Optimization Problem
50%
Geometric Structure
50%
Sparse Solution
50%
Learning Problems
50%
Composite Structures
50%
Optimal Convergence Rate
50%
Optimal Rate
50%
Random Selection
50%
Vanilla
50%
Learning Settings
50%
Sparse Learning
50%
Fast Training
50%
Strongly Convex Problems
50%
Practical Training
50%
Computer Science
Electronic Learning
100%
Sparsity
100%
Machine Learning
50%
Learning System
50%
Convergence Rate
50%
Optimization Problem
50%
Sparse Solution
50%
Learning Problem
50%
Baseline Method
50%
Random Selection
50%
Sparse Learning
50%
Engineering
Sparsity
100%
Convergence Rate
50%
Optimisation Problem
50%
Learning System
50%
Convex Problem
50%
Optimal Rate
50%
Random Selection
50%
Composite Structure
50%
Experimental Report
50%
Economics, Econometrics and Finance
Machine Learning
100%
Online Learning
100%
Mathematics
Stochastics
100%
Convergence Rate
33%
Convex Problem
33%
Chemical Engineering
Learning System
100%