# Generalization error bounds for kernel matrix completion and extrapolation

arXiv: https://arxiv.org/abs/1906.08770 · HTML: https://mlsanity.com/paper/1906.08770/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [Generalization Bounds for Inductive Matrix Completion in Low-noise Settings](https://mlsanity.com/paper/2212.08339/) (2022)
- [Information-Theoretic Generalization Bounds for Transductive Learning and its Applications](https://mlsanity.com/paper/2311.04561/) (2023)
- [Physical Backdoor Trigger Activation of Autonomous Vehicle using Reachability Analysis](https://mlsanity.com/paper/2303.13992/) (2023)

## Abstract (arXiv)

Prior information can be incorporated in matrix completion to improve estimation accuracy and extrapolate the missing entries. Reproducing kernel Hilbert spaces provide tools to leverage the said prior information, and derive more reliable algorithms. This paper analyzes the generalization error of such approaches, and presents numerical tests confirming the theoretical results.