Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits.
Hao ChenLili ZhengRaed Al KontarGarvesh RaskuttiPublished in: CoRR (2021)
Keyphrases
- gaussian process
- stochastic gradient descent
- online algorithms
- gaussian processes
- step size
- loss function
- regression model
- matrix factorization
- random forests
- convergence rate
- least squares
- approximate inference
- model selection
- hyperparameters
- bayesian framework
- convergence speed
- latent variables
- semi supervised
- learning algorithm
- probabilistic inference
- bayesian inference
- markov chain monte carlo
- support vector machine
- lower bound
- bayesian networks
- multiple kernel learning
- random sampling
- parameter settings
- machine learning
- image processing
- prior information