Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond.
Chun Kai LingKian Hsiang LowPatrick JailletPublished in: AAAI (2016)
Keyphrases
- gaussian process
- gaussian processes
- active learning
- semi supervised
- reward function
- gaussian process regression
- marginal likelihood
- fully bayesian
- regression model
- approximate inference
- model selection
- gaussian process models
- expectation propagation
- bayesian framework
- hyperparameters
- latent variables
- multiple agents
- posterior distribution
- learning algorithm
- random sampling
- bayesian methods
- inverse reinforcement learning
- reinforcement learning
- bayesian networks
- decision theory
- maximum likelihood
- supervised learning
- state space
- learning process