Variational Inference for Gaussian Process Models for Survival Analysis

Minyoung Kim, Vladimir Pavlovic
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:434-444, 2018.

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 uncer- tainty in the hazard function modeling while, at the same time, relaxing the time-covariates factorized assumption of the Cox model. How- ever, the existing MCMC inference algorithms for GPSAM have proven to be slow in prac- tice. In this paper we propose novel and scal- able 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 em- ploying 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 formu- lations, typically considered in survival analy- sis settings. The proposed approaches are eval- uated on two clinical and a divorce-marriage benchmark datasets, where we demonstrate improvements in prediction accuracy over the existing survival analysis methods, while re- ducing the complexity of inference compared to the recent state-of-the-art MCMC-based al- gorithms.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-kim18a, title = {Variational Inference for {G}aussian Process Models for Survival Analysis}, author = {Kim, Minyoung and Pavlovic, Vladimir}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {434--444}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/kim18a/kim18a.pdf}, url = {https://proceedings.mlr.press/r16/kim18a.html}, 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 uncer- tainty in the hazard function modeling while, at the same time, relaxing the time-covariates factorized assumption of the Cox model. How- ever, the existing MCMC inference algorithms for GPSAM have proven to be slow in prac- tice. In this paper we propose novel and scal- able 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 em- ploying 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 formu- lations, typically considered in survival analy- sis settings. The proposed approaches are eval- uated on two clinical and a divorce-marriage benchmark datasets, where we demonstrate improvements in prediction accuracy over the existing survival analysis methods, while re- ducing the complexity of inference compared to the recent state-of-the-art MCMC-based al- gorithms.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Variational Inference for Gaussian Process Models for Survival Analysis %A Minyoung Kim %A Vladimir Pavlovic %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-kim18a %I PMLR %P 434--444 %U https://proceedings.mlr.press/r16/kim18a.html %V R16 %X 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 uncer- tainty in the hazard function modeling while, at the same time, relaxing the time-covariates factorized assumption of the Cox model. How- ever, the existing MCMC inference algorithms for GPSAM have proven to be slow in prac- tice. In this paper we propose novel and scal- able 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 em- ploying 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 formu- lations, typically considered in survival analy- sis settings. The proposed approaches are eval- uated on two clinical and a divorce-marriage benchmark datasets, where we demonstrate improvements in prediction accuracy over the existing survival analysis methods, while re- ducing the complexity of inference compared to the recent state-of-the-art MCMC-based al- gorithms. %Z Reissued by PMLR on 04 October 2026.
APA
Kim, M. & Pavlovic, V.. (2018). Variational Inference for Gaussian Process Models for Survival Analysis. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:434-444 Available from https://proceedings.mlr.press/r16/kim18a.html. Reissued by PMLR on 04 October 2026.

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