aiqbench analysis
This paper advances Bayesian non-parametric methods by introducing amortized structured stochastic variational inference to improve uncertainty estimation in Gaussian Process Latent Variable Models. By relaxing mean-field assumptions, the approach yields a more flexible posterior that enhances reconstruction accuracy on learned data manifolds. The work represents a significant theoretical step forward for scalable probabilistic machine learning and manifold modeling.
Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models
arXiv:2610.03647v1 Announce Type: cross 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.
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.
Machine Learning (stat.ML); Machine Learning (cs.LG)
arXiv:2610.03647 [stat.ML]
(or arXiv:2610.03647v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2610.03647 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 236 words
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