Latent variable model for high-dimensional point process with structured missingness

Maksim Sinelnikov, Manuel Haussmann, Harri Lähdesmäki
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:45525-45543, 2024.

Abstract

Longitudinal data are important in numerous fields, such as healthcare, sociology and seismology, but real-world datasets present notable challenges for practitioners because they can be high-dimensional, contain structured missingness patterns, and measurement time points can be governed by an unknown stochastic process. While various solutions have been suggested, the majority of them have been designed to account for only one of these challenges. In this work, we propose a flexible and efficient latent-variable model that is capable of addressing all these limitations. Our approach utilizes Gaussian processes to capture correlations between samples and their associated missingness masks as well as to model the underlying point process. We construct our model as a variational autoencoder together with deep neural network parameterised decoder and encoder models, and develop a scalable amortised variational inference approach for efficient model training. We demonstrate competitive performance using both simulated and real datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-sinelnikov24a, title = {Latent variable model for high-dimensional point process with structured missingness}, author = {Sinelnikov, Maksim and Haussmann, Manuel and L\"{a}hdesm\"{a}ki, Harri}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {45525--45543}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/sinelnikov24a/sinelnikov24a.pdf}, url = {https://proceedings.mlr.press/v235/sinelnikov24a.html}, abstract = {Longitudinal data are important in numerous fields, such as healthcare, sociology and seismology, but real-world datasets present notable challenges for practitioners because they can be high-dimensional, contain structured missingness patterns, and measurement time points can be governed by an unknown stochastic process. While various solutions have been suggested, the majority of them have been designed to account for only one of these challenges. In this work, we propose a flexible and efficient latent-variable model that is capable of addressing all these limitations. Our approach utilizes Gaussian processes to capture correlations between samples and their associated missingness masks as well as to model the underlying point process. We construct our model as a variational autoencoder together with deep neural network parameterised decoder and encoder models, and develop a scalable amortised variational inference approach for efficient model training. We demonstrate competitive performance using both simulated and real datasets.} }
Endnote
%0 Conference Paper %T Latent variable model for high-dimensional point process with structured missingness %A Maksim Sinelnikov %A Manuel Haussmann %A Harri Lähdesmäki %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-sinelnikov24a %I PMLR %P 45525--45543 %U https://proceedings.mlr.press/v235/sinelnikov24a.html %V 235 %X Longitudinal data are important in numerous fields, such as healthcare, sociology and seismology, but real-world datasets present notable challenges for practitioners because they can be high-dimensional, contain structured missingness patterns, and measurement time points can be governed by an unknown stochastic process. While various solutions have been suggested, the majority of them have been designed to account for only one of these challenges. In this work, we propose a flexible and efficient latent-variable model that is capable of addressing all these limitations. Our approach utilizes Gaussian processes to capture correlations between samples and their associated missingness masks as well as to model the underlying point process. We construct our model as a variational autoencoder together with deep neural network parameterised decoder and encoder models, and develop a scalable amortised variational inference approach for efficient model training. We demonstrate competitive performance using both simulated and real datasets.
APA
Sinelnikov, M., Haussmann, M. & Lähdesmäki, H.. (2024). Latent variable model for high-dimensional point process with structured missingness. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:45525-45543 Available from https://proceedings.mlr.press/v235/sinelnikov24a.html.

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