Kernel Interpolation for Scalable Online Gaussian Processes
원문 초록
Gaussian processes (GPs) provide a gold standard for performance in online settings, such as sample-efficient control and black box optimization, where we need to update a posterior distribution as we acquire data in a sequential fashion. However, updating a GP posterior to accommodate even a single new observation after having observed $n$ points incurs at least $O(n)$ computations in the exact setting. We show how to use structured kernel interpolation to efficiently recycle computations for constant-time $O(1)$ online updates with respect to the number of points $n$, while retaining exact inference. We demonstrate the promise of our approach in a range of online regression and classification settings, Bayesian optimization, and active sampling to reduce error in malaria incidence forecasting. Code is available at https://github.com/wjmaddox/online_gp.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization
Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project
Causal Discovery via Bayesian Optimization
Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes
Constrained Bayesian Optimization under Bivariate Gaussian Process with Application to Cure Process Optimization
Wasserstein Barycenter Gaussian Process based Bayesian Optimization
토론
이 논문과 연결된 포럼 글이 아직 없습니다.