AI RESEARCH
Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processes
arXiv CS.LG
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ArXi:2507.05064v3 Announce Type: replace-cross Gaussian processes are flexible, probabilistic, non-parametric models widely used in machine learning and statistics. However, their scalability to large data sets is limited by computational constraints. To overcome these challenges, we propose Vecchia-inducing-points full-scale (VIF) approximations combining the strengths of global inducing points and local Vecchia approximations.