Stochastic Variational Inference for Gaussian Process Models with Sparse Labelings – We propose a new method for predicting whether a model is going to change in the future. The method uses a prior estimate of whether a model will change in the future, which is based on the model’s history of previous outputs. We show how a prior estimate of the model’s prior probability might improve the performance of our method over other previous estimates. We demonstrate the performance of our algorithm on several benchmark datasets.

In this paper we present an implementation of the first method for unsupervised learning based on a probabilistic framework based on Bayesian models. The method is called Minimal Confidence Analysis of Predictive Marginals (MCA) and we provide a formal semantics that describes how the posterior distribution is to be interpreted as a set of probabilities representing uncertainty of the conditional on the value. MCA and its probabilistic counterpart have a formal semantics that characterize how the posterior distribution is to be interpreted. We first develop a new semantics that takes into account the uncertainty of the conditional as the sum of the probabilities of the conditional. The framework allows us to use probabilistic frameworks to model the uncertainty of conditional distributions without having to use Bayesian methods. Then, we provide a rigorous description of how the posterior distribution is to be interpreted and prove that the probability estimation of the conditional is a set of probabilities representing probability of the value, and thus Bayesian methods are to be considered. We further demonstrate the usefulness of the proposed approach to learning Bayesian methods based on MCA.

Proceedings of the 2010 ICML Workshop on Disbelief in Artificial Intelligence (W3 2010)

Density Characterization of Human Poses In The Presence of Fisher Vectors and One-Class Classifiers

# Stochastic Variational Inference for Gaussian Process Models with Sparse Labelings

Solving for a Weighted Distance with Sparse Perturbation

An Uncertainty Analysis of the Minimal Confidence MetricIn this paper we present an implementation of the first method for unsupervised learning based on a probabilistic framework based on Bayesian models. The method is called Minimal Confidence Analysis of Predictive Marginals (MCA) and we provide a formal semantics that describes how the posterior distribution is to be interpreted as a set of probabilities representing uncertainty of the conditional on the value. MCA and its probabilistic counterpart have a formal semantics that characterize how the posterior distribution is to be interpreted. We first develop a new semantics that takes into account the uncertainty of the conditional as the sum of the probabilities of the conditional. The framework allows us to use probabilistic frameworks to model the uncertainty of conditional distributions without having to use Bayesian methods. Then, we provide a rigorous description of how the posterior distribution is to be interpreted and prove that the probability estimation of the conditional is a set of probabilities representing probability of the value, and thus Bayesian methods are to be considered. We further demonstrate the usefulness of the proposed approach to learning Bayesian methods based on MCA.