11,979 research outputs found
Sequential Gaussian Processes for Online Learning of Nonstationary Functions
Many machine learning problems can be framed in the context of estimating
functions, and often these are time-dependent functions that are estimated in
real-time as observations arrive. Gaussian processes (GPs) are an attractive
choice for modeling real-valued nonlinear functions due to their flexibility
and uncertainty quantification. However, the typical GP regression model
suffers from several drawbacks: i) Conventional GP inference scales
with respect to the number of observations; ii) updating a GP model
sequentially is not trivial; and iii) covariance kernels often enforce
stationarity constraints on the function, while GPs with non-stationary
covariance kernels are often intractable to use in practice. To overcome these
issues, we propose an online sequential Monte Carlo algorithm to fit mixtures
of GPs that capture non-stationary behavior while allowing for fast,
distributed inference. By formulating hyperparameter optimization as a
multi-armed bandit problem, we accelerate mixing for real time inference. Our
approach empirically improves performance over state-of-the-art methods for
online GP estimation in the context of prediction for simulated non-stationary
data and hospital time series data
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
A multivariate timeseries modeling approach to severity of illness assessment and forecasting in ICU with sparse, heterogeneous clinical data
The ability to determine patient acuity (or severity of illness) has immediate practical use for clinicians. We evaluate the use of multivariate timeseries modeling with the multi-task Gaussian process (GP) models using noisy, incomplete, sparse, heterogeneous and unevenly-sampled clinical data, including both physiological signals and clinical notes. The learned multi-task GP (MTGP) hyperparameters are then used to assess and forecast patient acuity. Experiments were conducted with two real clinical data sets acquired from ICU patients: firstly, estimating cerebrovascular pressure reactivity, an important indicator of secondary damage for traumatic brain injury patients, by learning the interactions between intracranial pressure and mean arterial blood pressure signals, and secondly, mortality prediction using clinical progress notes. In both cases, MTGPs provided improved results: an MTGP model provided better results than single-task GP models for signal interpolation and forecasting (0.91 vs 0.69 RMSE), and the use of MTGP hyperparameters obtained improved results when used as additional classification features (0.812 vs 0.788 AUC).Intel Science and Technology Center for Big DataNational Institutes of Health. (U.S.). National Library of Medicine (Biomedical Informatics Research Training Grant NIH/NLM 2T15 LM007092-22)National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01 Grant EB001659)Singapore. Agency for Science, Technology and Research (Graduate Scholarship
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
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