arXiv Statistics ML
Combining additivity and active subspaces for high-dimensional Gaussian process modeling
Read the original on arXiv Statistics ML →The Flow has not summarised this story yet — read it at arXiv Statistics ML.
Related stories
arXiv Machine Learning
Jul 23
A Bayesian Framework for Built-in Input Dimension Reduction for Gaussian Process Modeling
arXiv:2607. 19498v1 Announce Type: cross Abstract: Gaussian process (GP) modeling is widely used in computational science and engineering.
arXiv Machine Learning
Oct 5
Predictively Oriented Gaussian Process Posteriors
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...
arXiv Machine Learning
Aug 24
Exact and general decoupled solutions of the LMC Multitask Gaussian Process model
The paper presents an exact, efficient solution for the Linear Model of Co‑regionalization (LMC) multitask Gaussian Process by decoupling latent processes under a mild noise‑model assumption. It introduces a full parametrization of the resulting projected LMC, enabling linear‑time optimization and simplifying tasks such as training updates and leave‑one‑out cross‑validation. Experiments on synthetic and real data demonstrate that projected LMC is competitive with state‑of‑the‑art multitask GP models while offering greater interpretability and computational ease.
arXiv AI
Sep 28
Adaptive multi-resolution Gaussian processes: Scalable exact inference with naturally data-sparse covariance matrices
The paper introduces an adaptive multi‑resolution Gaussian process framework that achieves scalable, exact inference by constructing a naturally data‑sparse covariance matrix using basis functions anchored directly to samples. By shrinking the support domains of these basis functions, the resulting matrix has limited block sizes, ensuring sparsity and enabling efficient computation of its inverse via a sparse Cholesky algorithm. The authors demonstrate that this approach yields exact inference with training cost ≠≠ O(n log^2 n) and prediction cost ≠≠ O(log^d n), while also improving predictive uncertainties through an augmented basis function.
arXiv AI
Sep 18
Compressed Active Subspaces for Scalable Bayesian Inference
The paper introduces Compressed Active Subspaces (CAS), a scalable method for Bayesian inference in high‑dimensional models. CAS first compresses model parameters via a structured isometric embedding, then constructs the active subspace in this reduced space, dramatically lowering memory requirements. Experiments on neural networks of growing size show that CAS preserves predictive performance and provides robust uncertainty estimates while enabling inference where traditional active subspace methods fail.
arXiv Machine Learning
Oct 5
Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models
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.