Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent.
Jihao Andreas LinJavier AntoránShreyas PadhyDavid JanzJosé Miguel Hernández-LobatoAlexander TereninPublished in: CoRR (2023)
Keyphrases
- gaussian process
- stochastic gradient descent
- importance sampling
- approximate inference
- hyperparameters
- posterior distribution
- gaussian processes
- least squares
- latent variables
- random sampling
- matrix factorization
- bayesian framework
- step size
- regression model
- loss function
- markov chain monte carlo
- bayesian inference
- model selection
- random forests
- sample size
- support vector machine
- monte carlo
- multiple kernel learning
- semi supervised
- cross validation
- parameter space
- posterior probability
- closed form
- markov chain
- feature selection
- graph cuts
- maximum likelihood
- pairwise
- lower bound
- support vector