95 research outputs found
A Tutorial on Sparse Gaussian Processes and Variational Inference
Gaussian processes (GPs) provide a framework for Bayesian inference that can
offer principled uncertainty estimates for a large range of problems. For
example, if we consider regression problems with Gaussian likelihoods, a GP
model enjoys a posterior in closed form. However, identifying the posterior GP
scales cubically with the number of training examples and requires to store all
examples in memory. In order to overcome these obstacles, sparse GPs have been
proposed that approximate the true posterior GP with pseudo-training examples.
Importantly, the number of pseudo-training examples is user-defined and enables
control over computational and memory complexity. In the general case, sparse
GPs do not enjoy closed-form solutions and one has to resort to approximate
inference. In this context, a convenient choice for approximate inference is
variational inference (VI), where the problem of Bayesian inference is cast as
an optimization problem -- namely, to maximize a lower bound of the log
marginal likelihood. This paves the way for a powerful and versatile framework,
where pseudo-training examples are treated as optimization arguments of the
approximate posterior that are jointly identified together with hyperparameters
of the generative model (i.e. prior and likelihood). The framework can
naturally handle a wide scope of supervised learning problems, ranging from
regression with heteroscedastic and non-Gaussian likelihoods to classification
problems with discrete labels, but also multilabel problems. The purpose of
this tutorial is to provide access to the basic matter for readers without
prior knowledge in both GPs and VI. A proper exposition to the subject enables
also access to more recent advances (like importance-weighted VI as well as
interdomain, multioutput and deep GPs) that can serve as an inspiration for new
research ideas
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings
We introduce a novel kernel that models input-dependent couplings across
multiple latent processes. The pairwise joint kernel measures covariance along
inputs and across different latent signals in a mutually-dependent fashion. A
latent correlation Gaussian process (LCGP) model combines these non-stationary
latent components into multiple outputs by an input-dependent mixing matrix.
Probit classification and support for multiple observation sets are derived by
Variational Bayesian inference. Results on several datasets indicate that the
LCGP model can recover the correlations between latent signals while
simultaneously achieving state-of-the-art performance. We highlight the latent
covariances with an EEG classification dataset where latent brain processes and
their couplings simultaneously emerge from the model.Comment: 17 pages, 6 figures; accepted to ACML 201
Convolved Gaussian process priors for multivariate regression with applications to dynamical systems
In this thesis we address the problem of modeling correlated outputs using Gaussian process priors. Applications of modeling correlated outputs include the joint prediction of pollutant metals in geostatistics and multitask learning in machine learning. Defining a Gaussian process prior for correlated outputs translates into specifying a suitable covariance function that captures dependencies between the different output variables. Classical models for obtaining such a covariance function include the linear model of coregionalization and process convolutions. We propose a general framework for developing multiple output covariance functions by performing convolutions between smoothing kernels particular to each output and covariance functions that are common to all outputs. Both the linear model of coregionalization and the process convolutions turn out to be special cases of this framework. Practical aspects of the proposed methodology are studied in this thesis. They involve the use of domain-specific knowledge for defining relevant smoothing kernels, efficient approximations for reducing computational complexity and a novel method for establishing a general class of nonstationary covariances with applications in robotics and motion capture data.Reprints of the publications that appear at the end of this document, report case studies and experimental results in sensor networks, geostatistics and motion capture data that illustrate the performance of the different methods proposed.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Variational Inference of Joint Models using Multivariate Gaussian Convolution Processes
We present a non-parametric prognostic framework for individualized event
prediction based on joint modeling of both longitudinal and time-to-event data.
Our approach exploits a multivariate Gaussian convolution process (MGCP) to
model the evolution of longitudinal signals and a Cox model to map
time-to-event data with longitudinal data modeled through the MGCP. Taking
advantage of the unique structure imposed by convolved processes, we provide a
variational inference framework to simultaneously estimate parameters in the
joint MGCP-Cox model. This significantly reduces computational complexity and
safeguards against model overfitting. Experiments on synthetic and real world
data show that the proposed framework outperforms state-of-the art approaches
built on two-stage inference and strong parametric assumptions
Nonparametric Bayesian Mixed-effect Model: a Sparse Gaussian Process Approach
Multi-task learning models using Gaussian processes (GP) have been developed
and successfully applied in various applications. The main difficulty with this
approach is the computational cost of inference using the union of examples
from all tasks. Therefore sparse solutions, that avoid using the entire data
directly and instead use a set of informative "representatives" are desirable.
The paper investigates this problem for the grouped mixed-effect GP model where
each individual response is given by a fixed-effect, taken from one of a set of
unknown groups, plus a random individual effect function that captures
variations among individuals. Such models have been widely used in previous
work but no sparse solutions have been developed. The paper presents the first
sparse solution for such problems, showing how the sparse approximation can be
obtained by maximizing a variational lower bound on the marginal likelihood,
generalizing ideas from single-task Gaussian processes to handle the
mixed-effect model as well as grouping. Experiments using artificial and real
data validate the approach showing that it can recover the performance of
inference with the full sample, that it outperforms baseline methods, and that
it outperforms state of the art sparse solutions for other multi-task GP
formulations.Comment: Preliminary version appeared in ECML201
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