mlsanityData to 14 Oct 2026

Selective Classification via One-Sided Prediction

arXiv 2010.07853 ↗First posted 15 Oct 2020cs.LG stat.MLcited by 38

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 4; 2023: 13; 2024: 4; 2025: 6; 2026: 8.

38 papers.

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  1. 2021Machine Learning with a Reject Option: A surveycited by 109
  2. 2023Who Should Predict? Exact Algorithms For Learning to Defer to Humanscited by 70
  3. 2022MaskTune: Mitigating Spurious Correlations by Forcing to Explorecited by 66
  4. 2023When Does Confidence-Based Cascade Deferral Suffice?cited by 60
  5. 2021DOCTOR: A Simple Method for Detecting Misclassification Errorscited by 53
  6. 2022Sample Efficient Learning of Predictors that Complement Humanscited by 46
  7. 2023Principled Approaches for Learning to Defer with Multiple Expertscited by 38
  8. 2023Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithmscited by 36
  9. 2023Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstentioncited by 32
  10. 2024Realizable H-Consistent and Bayes-Consistent Loss Functions for Learning to Defercited by 26
  11. 2022Selective Prediction via Training Dynamicscited by 20
  12. 2024Agreement-Based Cascading for Efficient Inferencecited by 16
26 more
  1. 2025Mastering Multiple-Expert Routing: Realizable H-Consistency and Strong Guarantees for Learning to Defercited by 16
  2. 2023Plugin estimators for selective classification with out-of-distribution detectioncited by 14
  3. 2025Budgeted Multiple-Expert Deferralcited by 13
  4. 2023Regression with Cost-based Rejectioncited by 11
  5. 2021Online Selective Classification with Limited Feedbackcited by 9
  6. 2023Conservative Prediction via Data-Driven Confidence Minimizationcited by 8
  7. 2024A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problemscited by 5
  8. 2025Selective Risk Certification for LLM Outputs via Information-Lift Statistics: PAC-Bayes, Robustness, and Skeleton Designcited by 5
  9. 2025Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settingscited by 4
  10. 2022Selective classification using a robust meta-learning approachcited by 2
  11. 2023Counterfactually Comparing Abstaining Classifierscited by 2
  12. 2023Learning to Abstain From Uninformative Datacited by 2
  13. 2024Selective Explanationscited by 2
  14. 2025Interpretable and Fair Mechanisms for Abstaining Classifierscited by 2
  15. 2023A Minimax Approach Against Multi-Armed Adversarial Attacks Detectioncited by 0
  16. 2023Model Agnostic Explainable Selective Regression via Uncertainty Estimationcited by 0
  17. 2023Rescuing referral failures during automated diagnosis of domain-shifted medical imagescited by 0
  18. 2025The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classificationcited by 0
  19. 2026Measuring What Matters: Synthetic Benchmarks for Concept Bottleneck Modelscited by 0
  20. 2026Non-Degenerate Risk Certification for Automated Security Decisions: A Decision-Contract Theory with ATT&CK-Aligned Triage as a Worked Instancecited by 0
  21. 2026Decision Shifts, Lost Label Functionality, and an Inconclusive Grounding Audit in Correctness-Gated Multi-Teacher Distillationcited by 0
  22. 2026Refuse, Decompose, Refresh: A Claim-Safe Protocol for Closed-Loop AI Evaluationcited by 0
  23. 2026Classification with Abstention Under Class-Conditional Error Constraintscited by 0
  24. 2026When Are Aggregate Agent Traces Diagnosable? Traffic-Governed Interpretation and Calibrated Abstentioncited by 0
  25. 2026Estimation of the Label-Noise Transition Matrix with Performance Guarantees via Selective Classificationcited by 0
  26. 2026Lost in the Fallback: How Reliably Do Typed Decision Models Use Fallback Options?cited by 0