Abstract
Many machine learning methods aim to approximate the lower-dimensional manifold on which the data lives. A desirable feature of such methods is that they should capture the epistemic uncertainty of this learned manifold. One model that achieves this is the Gaussian Process Latent Variable Model, in which a Gaussian Process (GP) mapping from the latent space provides an estimate of the uncertainty of the manifold. However, the effectiveness of this uncertainty estimation is limited by the mean-field variational approximation between the GP inducing points and the latent variables. In this work, we apply Amortized Structured Stochastic Variational Inference to allow the variational posterior for the latent space to be conditionally dependent on the value of the inducing points. We demonstrate that this more flexible variational posterior improves several metrics relating to the reconstruction of points on the data manifold.
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Publication details
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Cite this article
APA 7
Tretiakov, M., Filippi, S., Fortuin, V., Misener, R., Sedgwick, R., & Odgers, J. (2026). Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models. https://omanscience.com/en/articles/amortized-structured-stochastic-variational-inference-for-gaussian-process-latent-variable-models
MLA 9
Tretiakov, Maksym, et al. "Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models." https://omanscience.com/en/articles/amortized-structured-stochastic-variational-inference-for-gaussian-process-latent-variable-models.
Chicago (author–date)
Tretiakov, Maksym, Sarah Filippi, Vincent Fortuin, Ruth Misener, Ruby Sedgwick, and James Odgers. 2026. "Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models." https://omanscience.com/en/articles/amortized-structured-stochastic-variational-inference-for-gaussian-process-latent-variable-models.
Harvard
Tretiakov, M., Filippi, S., Fortuin, V., Misener, R., Sedgwick, R. and Odgers, J. (2026) 'Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models', Available at: https://omanscience.com/en/articles/amortized-structured-stochastic-variational-inference-for-gaussian-process-latent-variable-models.
Vancouver
Tretiakov M, Filippi S, Fortuin V, Misener R, Sedgwick R, Odgers J. Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models. https://omanscience.com/en/articles/amortized-structured-stochastic-variational-inference-for-gaussian-process-latent-variable-models
IEEE
M. Tretiakov, S. Filippi, V. Fortuin, R. Misener, R. Sedgwick, and J. Odgers, "Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models," https://omanscience.com/en/articles/amortized-structured-stochastic-variational-inference-for-gaussian-process-latent-variable-models.