arXiv Machine Learning
Input Bias Correction in Gaussian Process Regression
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
Hugging Face Trending Papers
Oct 22
Input Bias Correction in Gaussian Process Regression
In traditional Gaussian process (GP) regression, the inputs are typically assumed to be known, and uncertainty enters only through the measurements. When scalar fields are mapped using GP regression w...
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 Computer Vision
Sep 23
Dual Covariance Gaussian Splatting SLAM: Decoupling Rendering and Registration for Robust Real-Time Tracking
arXiv:2609.25746v1 Announce Type: cross Abstract: ICP-based 3D Gaussian Splatting (3DGS) SLAM tracks in real time by registering incoming frames against map Gaussians, using each primitive's covarian...
arXiv Machine Learning
Sep 1
Information-Based Calibration of Uncertainty Quantification in Product-of-Experts Gaussian Process Models
arXiv:2608.29349v1 Announce Type: new Abstract: Gaussian process (GP) regression with a single global GP (GP-glo) incurs cubic computational cost, limiting scalability to large datasets. Product-of-e...
arXiv Machine Learning
Sep 15
Bayesian Optimisation Using Product-of-Experts Gaussian Process Models with Uncertainty Calibration
The paper introduces BO-pro-c, a Bayesian optimisation algorithm that employs a product-of-experts Gaussian process (GP-pro-c) as its surrogate model. GP-pro-c combines multiple local GP experts to improve uncertainty quantification, reduce computational cost, and preserve global correlations, addressing the cubic complexity of single global GP models. Experiments show that BO-pro-c achieves competitive optimisation performance with a 0.9% lower simple regret and a 39.4% reduction in computational overhead compared to a single‑global‑GP baseline.
arXiv Machine Learning
Oct 9
SCORE: Spectral Correlation Estimation for Multivariate Gaussians
The paper introduces SCORE, a scalable framework that learns an expressive covariance approximation for high‑dimensional Gaussian targets by combining scoring rule training with spectral‑space modeling. It decomposes the learning task into marginal distribution estimation and structured correlation matrix learning, achieving dense dependencies with linear storage and ≠(d log d) computational cost. The method employs a closed‑form Gaussian kernel score that remains valid for degenerate covariances, provides bounded gradients, and is invariant under unitary transforms, with theoretical guarantees on marginal recovery and correlation projection, and demonstrates improved performance on time‑series forecasting, monocular depth estimation, and spatial weather prediction.