# Selective Classification via One-Sided Prediction

arXiv: https://arxiv.org/abs/2010.07853 · HTML: https://mlsanity.com/paper/2010.07853/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 30 of 38)

- [Machine Learning with a Reject Option: A survey](https://mlsanity.com/paper/2107.11277/) (2021)
- [Who Should Predict? Exact Algorithms For Learning to Defer to Humans](https://mlsanity.com/paper/2301.06197/) (2023)
- [MaskTune: Mitigating Spurious Correlations by Forcing to Explore](https://mlsanity.com/paper/2210.00055/) (2022)
- [When Does Confidence-Based Cascade Deferral Suffice?](https://mlsanity.com/paper/2307.02764/) (2023)
- [DOCTOR: A Simple Method for Detecting Misclassification Errors](https://mlsanity.com/paper/2106.02395/) (2021)
- [Sample Efficient Learning of Predictors that Complement Humans](https://mlsanity.com/paper/2207.09584/) (2022)
- [Principled Approaches for Learning to Defer with Multiple Experts](https://mlsanity.com/paper/2310.14774/) (2023)
- [Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms](https://mlsanity.com/paper/2310.14772/) (2023)
- [Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention](https://mlsanity.com/paper/2310.14770/) (2023)
- [Realizable H-Consistent and Bayes-Consistent Loss Functions for Learning to Defer](https://mlsanity.com/paper/2407.13732/) (2024)
- [Selective Prediction via Training Dynamics](https://mlsanity.com/paper/2205.13532/) (2022)
- [Agreement-Based Cascading for Efficient Inference](https://mlsanity.com/paper/2407.02348/) (2024)
- [Mastering Multiple-Expert Routing: Realizable H-Consistency and Strong Guarantees for Learning to Defer](https://mlsanity.com/paper/2506.20650/) (2025)
- [Plugin estimators for selective classification with out-of-distribution detection](https://mlsanity.com/paper/2301.12386/) (2023)
- [Budgeted Multiple-Expert Deferral](https://mlsanity.com/paper/2510.26706/) (2025)
- [Regression with Cost-based Rejection](https://mlsanity.com/paper/2311.04550/) (2023)
- [Online Selective Classification with Limited Feedback](https://mlsanity.com/paper/2110.14243/) (2021)
- [Conservative Prediction via Data-Driven Confidence Minimization](https://mlsanity.com/paper/2306.04974/) (2023)
- [A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems](https://mlsanity.com/paper/2407.12710/) (2024)
- [Selective Risk Certification for LLM Outputs via Information-Lift Statistics: PAC-Bayes, Robustness, and Skeleton Design](https://mlsanity.com/paper/2509.12527/) (2025)
- [Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings](https://mlsanity.com/paper/2505.22356/) (2025)
- [Selective classification using a robust meta-learning approach](https://mlsanity.com/paper/2212.05987/) (2022)
- [Counterfactually Comparing Abstaining Classifiers](https://mlsanity.com/paper/2305.10564/) (2023)
- [Learning to Abstain From Uninformative Data](https://mlsanity.com/paper/2309.14240/) (2023)
- [Selective Explanations](https://mlsanity.com/paper/2405.19562/) (2024)
- [Interpretable and Fair Mechanisms for Abstaining Classifiers](https://mlsanity.com/paper/2503.18826/) (2025)
- [A Minimax Approach Against Multi-Armed Adversarial Attacks Detection](https://mlsanity.com/paper/2302.02216/) (2023)
- [Model Agnostic Explainable Selective Regression via Uncertainty Estimation](https://mlsanity.com/paper/2311.09145/) (2023)
- [Rescuing referral failures during automated diagnosis of domain-shifted medical images](https://mlsanity.com/paper/2311.16766/) (2023)
- [The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification](https://mlsanity.com/paper/2510.01020/) (2025)

## Abstract (arXiv)

We propose a novel method for selective classification (SC), a problem which allows a classifier to abstain from predicting some instances, thus trading off accuracy against coverage (the fraction of instances predicted). In contrast to prior gating or confidence-set based work, our proposed method optimises a collection of class-wise decoupled one-sided empirical risks, and is in essence a method for explicitly finding the largest decision sets for each class that have few false positives. This one-sided prediction (OSP) based relaxation yields an SC scheme that attains near-optimal coverage in the practically relevant high target accuracy regime, and further admits efficient implementation, leading to a flexible and principled method for SC. We theoretically derive generalization bounds for SC and OSP, and empirically we show that our scheme strongly outperforms state of the art methods in coverage at small error levels.