# Kernel Machines With Missing Responses

arXiv: https://arxiv.org/abs/1806.02865 · HTML: https://mlsanity.com/paper/1806.02865/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2018.

## Built on by (most cited first, 2 of 2)

- [Don't fear the unlabelled: safe semi-supervised learning via simple debiasing](https://mlsanity.com/paper/2203.07512/) (2022)
- [Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanism](https://mlsanity.com/paper/2302.07540/) (2023)

## Abstract (arXiv)

Missing responses is a missing data format in which outcomes are not always observed. In this work we develop kernel machines that can handle missing responses. First, we propose a kernel machine family that uses mainly the complete cases. For the quadratic loss, we then propose a family of doubly-robust kernel machines. The proposed kernel-machine estimators can be applied to both regression and classification problems. We prove oracle inequalities for the finite-sample differences between the kernel machine risk and Bayes risk. We use these oracle inequalities to prove consistency and to calculate convergence rates. We demonstrate the performance of the two proposed kernel machine families using both a simulation study and a real-world data analysis.