# Learning Confidence Sets using Support Vector Machines

arXiv: https://arxiv.org/abs/1809.10818 · HTML: https://mlsanity.com/paper/1809.10818/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2018.

## Built on by (most cited first, 5 of 5)

- [Efficient Online Set-valued Classification with Bandit Feedback](https://mlsanity.com/paper/2405.04393/) (2024)
- [Classification Under Ambiguity: When Is Average-K Better Than Top-K?](https://mlsanity.com/paper/2112.08851/) (2021)
- [Set Valued Predictions For Robust Domain Generalization](https://mlsanity.com/paper/2507.03146/) (2025)
- [MPC-guided Imitation Learning of Neural Network Policies for the Artificial Pancreas](https://mlsanity.com/paper/2003.01283/) (2020)
- [Learning Acceptance Regions for Many Classes with Anomaly Detection](https://mlsanity.com/paper/2209.09963/) (2022)

## Abstract (arXiv)

The goal of confidence-set learning in the binary classification setting is to construct two sets, each with a specific probability guarantee to cover a class. An observation outside the overlap of the two sets is deemed to be from one of the two classes, while the overlap is an ambiguity region which could belong to either class. Instead of plug-in approaches, we propose a support vector classifier to construct confidence sets in a flexible manner. Theoretically, we show that the proposed learner can control the non-coverage rates and minimize the ambiguity with high probability. Efficient algorithms are developed and numerical studies illustrate the effectiveness of the proposed method.