# Semi-Parametric Uncertainty Bounds for Binary Classification

arXiv: https://arxiv.org/abs/1903.09790 · HTML: https://mlsanity.com/paper/1903.09790/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2019.

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

- [Exact Distribution-Free Hypothesis Tests for the Regression Function of Binary Classification via Conditional Kernel Mean Embeddings](https://mlsanity.com/paper/2103.05126/) (2021)
- [Data-Driven Upper Confidence Bounds with Near-Optimal Regret for Heavy-Tailed Bandits](https://mlsanity.com/paper/2406.05710/) (2024)
- [Recursive Estimation of Conditional Kernel Mean Embeddings](https://mlsanity.com/paper/2302.05955/) (2023)
- [Resampled Confidence Regions with Exponential Shrinkage for the Regression Function of Binary Classification](https://mlsanity.com/paper/2308.01835/) (2023)
- [Robust Independence Tests with Finite Sample Guarantees for Synchronous Stochastic Linear Systems](https://mlsanity.com/paper/2308.02054/) (2023)

## Abstract (arXiv)

The paper studies binary classification and aims at estimating the underlying regression function which is the conditional expectation of the class labels given the inputs. The regression function is the key component of the Bayes optimal classifier, moreover, besides providing optimal predictions, it can also assess the risk of misclassification. We aim at building non-asymptotic confidence regions for the regression function and suggest three kernel-based semi-parametric resampling methods. We prove that all of them guarantee regions with exact coverage probabilities and they are strongly consistent.