Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications.
Alexandre CaponeArmin LedererSandra HirchePublished in: ICML (2022)
Keyphrases
- error bounds
- gaussian process
- hyperparameters
- safety critical
- model selection
- cross validation
- gaussian processes
- bayesian framework
- closed form
- bayesian inference
- random sampling
- support vector
- posterior distribution
- prior information
- sample size
- noise level
- formal methods
- theoretical analysis
- incremental learning
- maximum likelihood
- em algorithm
- worst case
- gaussian process regression
- maximum a posteriori
- incomplete data
- marginal likelihood
- variational bayes
- covariance function
- parameter space
- parameter settings
- regression model
- machine learning
- missing values
- noise reduction
- probabilistic model
- image reconstruction
- active learning
- upper bound
- supervised learning
- markov random field
- generative model
- semi supervised
- expectation maximization
- lower bound
- image segmentation
- decision trees
- image processing
- expectation propagation
- feature selection
- noisy images