74 search results - page 8 / 15 » Regret Bounds for Gaussian Process Bandit Problems |
CIMCA
2008
IEEE
14 years 2 months ago
2008
IEEE
Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, ...
NIPS
2004
13 years 9 months ago
2004
We consider an MDP setting in which the reward function is allowed to change during each time step of play (possibly in an adversarial manner), yet the dynamics remain fixed. Simi...
CORR
2010
Springer
13 years 6 months ago
2010
Springer
We consider model-based reinforcement learning in finite Markov Decision Processes (MDPs), focussing on so-called optimistic strategies. Optimism is usually implemented by carryin...
JMLR
2010
13 years 2 months ago
2010
We introduce a variational inference framework for training the Gaussian process latent variable model and thus performing Bayesian nonlinear dimensionality reduction. This method...
JAT
2007
13 years 7 months ago
2007
We prove sharp bounds for the expectation of the supremum of the Gaussian process indexed by the intersection of Bn p with ρBn q for 1 ≤ p, q ≤ ∞ and ρ > 0, and by the ...