Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data

Yu Gong, Hossein Hajimirsadeghi, Jiawei He, Thibaut Durand, Greg Mori
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:2377-2385, 2021.

Abstract

Learning from heterogeneous data poses challenges such as combining data from various sources and of different types. Meanwhile, heterogeneous data are often associated with missingness in real-world applications due to heterogeneity and noise of input sources. In this work, we propose the variational selective autoencoder (VSAE), a general framework to learn representations from partially-observed heterogeneous data. VSAE learns the latent dependencies in heterogeneous data by modeling the joint distribution of observed data, unobserved data, and the imputation mask which represents how the data are missing. It results in a unified model for various downstream tasks including data generation and imputation. Evaluation on both low-dimensional and high-dimensional heterogeneous datasets for these two tasks shows improvement over state-of-the-art models.

Cite this Paper

BibTeX
@InProceedings{pmlr-v130-gong21a, title = { Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data }, author = {Gong, Yu and Hajimirsadeghi, Hossein and He, Jiawei and Durand, Thibaut and Mori, Greg}, booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics}, pages = {2377--2385}, year = {2021}, editor = {Banerjee, Arindam and Fukumizu, Kenji}, volume = {130}, series = {Proceedings of Machine Learning Research}, month = {13--15 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v130/gong21a/gong21a.pdf}, url = {https://proceedings.mlr.press/v130/gong21a.html}, abstract = { Learning from heterogeneous data poses challenges such as combining data from various sources and of different types. Meanwhile, heterogeneous data are often associated with missingness in real-world applications due to heterogeneity and noise of input sources. In this work, we propose the variational selective autoencoder (VSAE), a general framework to learn representations from partially-observed heterogeneous data. VSAE learns the latent dependencies in heterogeneous data by modeling the joint distribution of observed data, unobserved data, and the imputation mask which represents how the data are missing. It results in a unified model for various downstream tasks including data generation and imputation. Evaluation on both low-dimensional and high-dimensional heterogeneous datasets for these two tasks shows improvement over state-of-the-art models. } }
Endnote
%0 Conference Paper %T Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data %A Yu Gong %A Hossein Hajimirsadeghi %A Jiawei He %A Thibaut Durand %A Greg Mori %B Proceedings of The 24th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2021 %E Arindam Banerjee %E Kenji Fukumizu %F pmlr-v130-gong21a %I PMLR %P 2377--2385 %U https://proceedings.mlr.press/v130/gong21a.html %V 130 %X Learning from heterogeneous data poses challenges such as combining data from various sources and of different types. Meanwhile, heterogeneous data are often associated with missingness in real-world applications due to heterogeneity and noise of input sources. In this work, we propose the variational selective autoencoder (VSAE), a general framework to learn representations from partially-observed heterogeneous data. VSAE learns the latent dependencies in heterogeneous data by modeling the joint distribution of observed data, unobserved data, and the imputation mask which represents how the data are missing. It results in a unified model for various downstream tasks including data generation and imputation. Evaluation on both low-dimensional and high-dimensional heterogeneous datasets for these two tasks shows improvement over state-of-the-art models.
APA
Gong, Y., Hajimirsadeghi, H., He, J., Durand, T. & Mori, G.. (2021). Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data . Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 130:2377-2385 Available from https://proceedings.mlr.press/v130/gong21a.html.

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