arXiv:2610.03201v1 Announce Type: cross
Abstract: Gaussian Processes (GPs) are a powerful tool for modelling and quantifying uncertainty in functional relationships. However, they require practitione...
By Callum Lau, Jeremias Knoblauch, Louis Sharrock
The paper introduces Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models, enabling the variational posterior over latent variables to depend on GP inducing points. This approach addresses limitations of mean‑field approximations and yields improved reconstruction metrics for data manifold points.
By Maksym Tretiakov, Sarah Lucie Filipp, Vincent Fortuin, Ruth Misener, Ruby Sedgwick, James Odgers
The paper introduces Personalized Federated Hierarchical Gaussian Processes (pFedHGP), a method for probabilistic regression and classification on data distributed across heterogeneous clients. Each client’s latent function is split into a shared global component, a client‑specific deviation that shares the global kernel, and a flexible local residual. Using sparse inducing‑variable approximations and federated variational inference, raw data remain local while the server exchanges only low‑dimensional statistics, enabling full predictive distributions for uncertainty‑aware decisions. In experiments, pFedHGP achieves perfect fault classification in press tonnage monitoring with only 13.77% of labeled cycles and accurately recovers geographic zones in federated air‑quality modeling without centralizing station‑level time series.
By Xianjian Xie, Hao Yan
arXiv:2607. 19498v1 Announce Type: cross Abstract: Gaussian process (GP) modeling is widely used in computational science and engineering.
By Eric Herrison Gyamfi, Emily L. Kang, Bledar A. Konomi, Guang Lin
arXiv:2608. 13418v1 Announce Type: cross Abstract: Given a dataset where a portion of the samples are contaminated, our goal is to recover the underlying clean population distribution.
By Yikai Xu, Zhao Chen, Jian Huang
arXiv:2609.16472v1 Announce Type: new
Abstract: Warped Gaussian processes (GPs) handle non-Gaussian observations by mapping them into a latent standard GP via a parametric transformation called warpi...
By Emilio Ruiz-Moreno, Konstantinos Slavakis, Baltasar Beferull-Lozano