The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …

A key problem in statistical modeling is model selection, that is, how to choose a model at an
appropriate level of complexity. This problem appears in many settings, most prominently in …

Abstract To scale Gaussian processes (GPs) to large data sets we introduce the robust
Bayesian Committee Machine (rBCM), a practical and scalable product-of-experts model for …

In this paper, we provide a comprehensive survey of the mixture of experts (ME). We discuss
the fundamental models for regression and classification and also their training with the …

Forecasting the evolution of complex systems is noted as one of the 10 grand challenges of
modern science. Time series data from complex systems capture the dynamic behaviors and …

We introduce a new nonlinear model for classification, in which we model the joint
distribution of response variable, y, and covariates, x, non-parametrically using Dirichlet …

The most difficult—and often most essential—aspect of many interception and tracking tasks
is constructing motion models of the targets. Experts rarely can provide complete information …

With the emergence of more challenging contexts for robotics, the mechanical design of
robots is becoming more and more complex. Moreover, their missions often involve …

This paper proposes a new variational approximation for infinite mixtures of Gaussian
processes. As an extension of the single Gaussian process regression model, mixtures of …

Theories of how people learn relationships between continuous variables have tended to
focus on two possibilities: one, that people are estimating explicit functions, or two that they …