Variational inference for Gaussian process models for survival analysis

Minyoung Kim, Vladimir Pavlovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Gaussian process survival analysis model (GP-SAM) was recently proposed to address key deficiencies of the Cox proportional hazard model, namely the need to account for uncertainty in the hazard function modeling while, at the same time, relaxing the time-covariates factorized assumption of the Cox model. However, the existing MCMC inference algorithms for GPSAM have proven to be slow in practice. In this paper we propose novel and scalable variational inference algorithms for GP-SAM that reduce the time complexity of the sampling approaches and improve scalability to large datasets. We accomplish this by employing two effective strategies in scalable GP: i) using pseudo inputs and ii) approximation via random feature expansions. In both setups, we derive the full and partial likelihood formulations, typically considered in survival analysis settings. The proposed approaches are evaluated on two clinical and a divorce-marriage benchmark datasets, where we demonstrate improvements in prediction accuracy over the existing survival analysis methods, while reducing the complexity of inference compared to the recent state-of-the-art MCMC-based algorithms.

Original languageEnglish (US)
Title of host publication34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018
EditorsAmir Globerson, Amir Globerson, Ricardo Silva
PublisherAssociation For Uncertainty in Artificial Intelligence (AUAI)
Pages435-445
Number of pages11
ISBN (Electronic)9781510871601
StatePublished - Jan 1 2018
Event34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018 - Monterey, United States
Duration: Aug 6 2018Aug 10 2018

Publication series

Name34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018
Volume1

Other

Other34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018
CountryUnited States
CityMonterey
Period8/6/188/10/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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