Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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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The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: eess
34
The categories of this paper are: chem-ph
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The categories of this paper are: funct-an
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom