Functional central limit theorem and strong law of large numbers for stochastic gradient Langevin dynamics
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Publication:6073851
DOI10.1007/s00245-023-10052-yarXiv2210.02092OpenAlexW4386223191MaRDI QIDQ6073851
Publication date: 18 September 2023
Published in: Applied Mathematics and Optimization (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2210.02092
functional central limit theoremonline learningmixingstochastic gradient descentMarkov chains in random environments
Cites Work
- Unnamed Item
- Markov chains and stochastic stability
- A functional central limit theorem for weakly dependent sequences of random variables
- Central limit theorems under weak dependence
- Markov chains in random environment with applications in queuing theory and machine learning
- Invariant measures for multidimensional fractional stochastic volatility models
- On stochastic gradient Langevin dynamics with dependent data streams in the logconcave case
- (Non-) asymptotic properties of Stochastic Gradient Langevin Dynamics
- Stochastic approximation with averaging innovation applied to Finance
- On the Averaged Stochastic Approximation for Linear Regression
- On Stochastic Gradient Langevin Dynamics with Dependent Data Streams: The Fully Nonconvex Case