Gaussian Process Parameter Estimation Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits.
Hao ChenLili ZhengRaed Al KontarGarvesh RaskuttiPublished in: J. Mach. Learn. Res. (2022)
Keyphrases
- parameter estimation
- gaussian process
- stochastic gradient descent
- least squares
- model selection
- online algorithms
- hyperparameters
- gaussian processes
- approximate inference
- regression model
- maximum likelihood
- step size
- convergence rate
- matrix factorization
- loss function
- parameter values
- bayesian framework
- semi supervised
- machine learning
- latent variables
- posterior distribution
- cross validation
- random forests
- em algorithm
- markov random field
- support vector machine
- expectation maximization
- sample size
- lower bound
- decision trees
- bayesian inference
- learning algorithm