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Constrained Langevin Algorithms with L-mixing External Random Variables
Yuping Zheng
,
Andrew Lamperski
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
2
Scopus citations
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Keyphrases
Random Variables
100%
L-mixing
100%
Langevin Algorithm
100%
Constrained Problems
50%
Wasserstein Distance
50%
Non-convex Cost Function
50%
Machine Learning
25%
Sampling Device
25%
Sample Learning
25%
Additive Noise
25%
Gradient Method
25%
Monte Carlo (MC) Simulation
25%
Target Distribution
25%
Sampling Optimization
25%
Convex Domain
25%
Non-asymptotic Analysis
25%
Unconstrained Problems
25%
Polyhedral Constraints
25%
Variable Constraints
25%
Monte Carlo Optimization
25%
Mixing Data
25%
Non-convex Learning
25%
Mathematics
Wasserstein Distance
100%
Random Variable
100%
Markov Chain Monte Carlo
50%
Convex Domain
50%
target distribution π
50%
Asymptotic Analysis
50%
Additive Noise
50%
Computer Science
Data Variable
100%
Random Variable
100%
Target Distribution
50%
markov chain monte-carlo
50%
Gradient Descent Method
50%
Machine Learning
50%
Learning System
50%
Engineering
Random Variable ξ
100%
Constrained Problem
100%
Additive Noise
50%
Gradient Descent Method
50%
Learning System
50%
Earth and Planetary Sciences
Machine Learning
100%
Markov Chain Monte Carlo
100%