Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models
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 posterio...
| Authors | Maksym Tretiakov, Sarah Filippi, Vincent Fortuin et al. |
|---|---|
| Primary category | stat.ML |
| Published | 2 October 2026 |
| Last updated at source | 9 October 2026 |
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