arXiv:2110.02296
On the Correspondence between Gaussian Processes and Geometric Harmonics
원문 초록
We discuss the correspondence between Gaussian process regression and Geometric Harmonics, two similar kernel-based methods that are typically used in different contexts. Research communities surrounding the two concepts often pursue different goals. Results from both camps can be successfully combined, providing alternative interpretations of uncertainty in terms of error estimation, or leading towards accelerated Bayesian Optimization due to dimensionality reduction.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization
Active Learning for Manifold Gaussian Process Regression
AKRMap: Adaptive Kernel Regression for Trustworthy Visualization of Cross-Modal Embeddings
Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project
Distributional encoding for Gaussian process regression with qualitative inputs
Learning Treatment Representations for Downstream Instrumental Variable Regression
토론
이 논문과 연결된 포럼 글이 아직 없습니다.