IEEE

Multiple Model Recursive Gaussian Process for Robust Target Tracking

Abstract: Accurate tracking of targets is vital for safe and reliable operations, particularly in complex and dynamic environments such as urban areas. Traditional tracking methods, including Kalman ...
Nature

Gaussian process regression with physics-guided pseudo-sample augmentation for wear prediction under sparse measurements in milling

Tool wear prediction is essential to ensure machining quality and sustainability. Hybrid physics-data Gaussian process regression (GPR) methods integrate domain knowledge with data-driven learning, ...
IEEE

Sparse Variational Information Bottleneck Gaussian Processes for Uncertainty Estimation

Abstract: Inducing-point-based sparse variational approximation scales Gaussian process models to large datasets but tends to overestimate observation noise and underestimate posterior variance.
GitHub

[ICML 2026] SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning

SIKA-GP is a PyTorch/GPyTorch codebase for accelerating Gaussian process (GP) inference using Sparse Inducing Kernel Approximations. SIKA-GP builds sparsely activated Laplace-kernel basis functions on ...