Feature Space Adaptation for Effortless Gaussian Process Flows

arXiv:2610.11459 · stat.ML, cs.LG, stat.ME · Submitted 2026-10-08 · Read on arXiv

stat.ML, cs.LG, stat.ME

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Adaptation — 23× in this paper · explained in 1 episode
• Adam — 8× in this paper · explained in 2 episodes
• PDE residual — 6× in this paper · explained in 2 episodes
• Reconstruction Error — 5× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Generative AI — 4× in this paper · explained in 2 episodes
• Adaptive Sampling — 3× in this paper · explained in 1 episode
• Ablation — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Gaussian Processes (GPs) — 1× in this paper · explained in 2 episodes
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Kernel decomposition — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Online Adaptation — 1× in this paper · explained in 3 episodes
• Ornstein–Uhlenbeck Process — 1× in this paper · explained in 3 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes

Sources

• Gaussian Processes for Big Data
• Neural PDE Solvers for Irregular Domains
• Adam: A Method for Stochastic Optimization
• Conditioning Gaussian Processes on Almost Anything
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• LatentFlow: A General Framework for Conditioning Stochastic Processes
• Efficiently Sampling Functions from Gaussian Process Posteriors

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