Variational zero-inflated Gaussian processes with sparse kernels

Pashupati Hegde, Markus Heinonen, Samuel Kaski
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:360-370, 2018.

Abstract

Zero-inflated datasets, which have an ex- cess of zero outputs, are commonly en- countered in problems such as climate or rare event modelling. Conventional ma- chine learning approaches tend to overesti- mate the non-zeros leading to poor perfor- mance. We propose a novel model family of zero-inflated Gaussian processes (ZiGP) for such zero-inflated datasets, produced by sparse kernels through learning a la- tent probit Gaussian process that can zero out kernel rows and columns whenever the signal is absent. The ZiGPs are particu- larly useful for making the powerful Gaus- sian process networks more interpretable. We introduce sparse GP networks where variable-order latent modelling is achieved through sparse mixing signals. We derive the non-trivial stochastic variational infer- ence tractably for scalable learning of the sparse kernels in both models. The novel output-sparse approach improves both pre- diction of zero-inflated data and inter- pretability of latent mixing models.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-hegde18a, title = {Variational zero-inflated {G}aussian processes with sparse kernels}, author = {Hegde, Pashupati and Heinonen, Markus and Kaski, Samuel}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {360--370}, 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/hegde18a/hegde18a.pdf}, url = {https://proceedings.mlr.press/r16/hegde18a.html}, abstract = {Zero-inflated datasets, which have an ex- cess of zero outputs, are commonly en- countered in problems such as climate or rare event modelling. Conventional ma- chine learning approaches tend to overesti- mate the non-zeros leading to poor perfor- mance. We propose a novel model family of zero-inflated Gaussian processes (ZiGP) for such zero-inflated datasets, produced by sparse kernels through learning a la- tent probit Gaussian process that can zero out kernel rows and columns whenever the signal is absent. The ZiGPs are particu- larly useful for making the powerful Gaus- sian process networks more interpretable. We introduce sparse GP networks where variable-order latent modelling is achieved through sparse mixing signals. We derive the non-trivial stochastic variational infer- ence tractably for scalable learning of the sparse kernels in both models. The novel output-sparse approach improves both pre- diction of zero-inflated data and inter- pretability of latent mixing models.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Variational zero-inflated Gaussian processes with sparse kernels %A Pashupati Hegde %A Markus Heinonen %A Samuel Kaski %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-hegde18a %I PMLR %P 360--370 %U https://proceedings.mlr.press/r16/hegde18a.html %V R16 %X Zero-inflated datasets, which have an ex- cess of zero outputs, are commonly en- countered in problems such as climate or rare event modelling. Conventional ma- chine learning approaches tend to overesti- mate the non-zeros leading to poor perfor- mance. We propose a novel model family of zero-inflated Gaussian processes (ZiGP) for such zero-inflated datasets, produced by sparse kernels through learning a la- tent probit Gaussian process that can zero out kernel rows and columns whenever the signal is absent. The ZiGPs are particu- larly useful for making the powerful Gaus- sian process networks more interpretable. We introduce sparse GP networks where variable-order latent modelling is achieved through sparse mixing signals. We derive the non-trivial stochastic variational infer- ence tractably for scalable learning of the sparse kernels in both models. The novel output-sparse approach improves both pre- diction of zero-inflated data and inter- pretability of latent mixing models. %Z Reissued by PMLR on 04 October 2026.
APA
Hegde, P., Heinonen, M. & Kaski, S.. (2018). Variational zero-inflated Gaussian processes with sparse kernels. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:360-370 Available from https://proceedings.mlr.press/r16/hegde18a.html. Reissued by PMLR on 04 October 2026.

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