Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
Vidhi LalchandWessel P. BruinsmaDavid R. BurtCarl Edward RasmussenPublished in: NeurIPS (2022)
Keyphrases
- gaussian process
- hyperparameters
- covariance function
- model selection
- gaussian processes
- cross validation
- bayesian framework
- gaussian process regression
- closed form
- bayesian inference
- support vector
- random sampling
- em algorithm
- prior information
- maximum likelihood
- noise level
- approximate inference
- posterior distribution
- sample size
- maximum a posteriori
- variational bayes
- regression model
- incremental learning
- incomplete data
- bayesian methods
- high dimensional
- marginal likelihood
- parameter space
- sparse representation
- parameter estimation
- prior knowledge
- grid search
- parameter settings
- missing values
- semi supervised
- upper bound
- expectation propagation
- latent variables
- expectation maximization
- higher order
- probabilistic model