JMLR
2010
12 years 11 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...
JMLR
2010
12 years 11 months ago
2010
Bandit algorithms are concerned with trading exploration with exploitation where a number of options are available but we can only learn their quality by experimenting with them. ...
JMLR
2010
12 years 11 months ago
2010
Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Mo...
AROBOTS
2011
12 years 11 months ago
2011
Abstract— GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filt...
ICONIP
2009
13 years 2 months ago
2009
It is generally assumed in the traditional formulation of supervised learning that only the outputs data are uncertain. However, this assumption might be too strong for some learni...
ICDM
2010
IEEE
13 years 2 months ago
2010
IEEE
In this paper, we study probabilistic modeling of heterogeneously attributed multi-dimensional arrays. The model can manage the heterogeneity by employing an individual exponential...
JMS
2010
13 years 3 months ago
2010
This work studies the impact of using dynamic information as features in a machine learning algorithm for the prediction task of classifying critically ill patients in two classes ...
NECO
2000
13 years 4 months ago
2000
The Bayesian committee machine (BCM) is a novel approach to combining estimators which were trained on different data sets. Although the BCM can be applied to the combination of a...
JAT
2007
13 years 4 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 ...
PAMI
2006
13 years 4 months ago
2006
Gaussian process classifiers (GPCs) are Bayesian probabilistic kernel classifiers. In GPCs, the probability of belonging to a certain class at an input location is monotonically re...