The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on linear dependency between input features and output values.Expand

We propose a fully nonparametric ABC paradigm which circumvents the need for manually selecting summary statistics for models with intractable likelihoods.Expand

Two semimetrics on probability distributions are proposed, given as the sum of differences of expectations of analytic functions evaluated at spatial or frequency locations.Expand

We propose squared-loss mutual information regularization (SMIR) for multi-class probabilistic classification, following the information maximization principle.Expand

We introduce the Locally Linear Latent Variable Model (LL-LVM), a probabilistic model for non-linear manifold discovery that describes a joint distribution over observations, their manifold coordinates and locally linear maps conditioned on a set of neighbourhood relationships.Expand

In kernel methods, the median heuristic has been widely used as a way of setting the bandwidth of RBF kernels. While its empirical performances make it a safe choice under many circumstances, there… Expand

We propose an efficient nonparametric strategy for learning a message operator in expectation propagation, which takes as input the set of incoming messages to a factor node, and produces an outgoing message as output.Expand