ForecastingUS & West
Scalable Gaussian Process with Trigonometric Features for Safe MPC
Researchers developed DTF-GP, a finite-dimensional kernel approximation based on deterministic trigonometric features. This approach enables uniform uncertainty bounds needed for safety guarantees in learning-based MPC while scaling to large datasets.
Why it matters
Important for scalable learning-based control with safety guarantees in large-scale systems.
- Original headline
- Scalable Gaussian Process Regression via Deterministic Trigonometric Features: Uniform Bounds for Safe Model Predictive Control
Translation and summary are machine-generated from the source. The full article is not republished; its rights belong to the source.