mlsanityData to 7 Oct 2026

SelectiveNet: A Deep Neural Network with an Integrated Reject Option

arXiv 1901.09192 ↗First posted 26 Jan 2019cs.LG stat.MLcited by 333

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Cited by 20 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We consider the problem of selective prediction (also known as reject option) in deep neural networks, and introduce SelectiveNet, a deep neural architecture with an integrated reject option. Existing rejection mechanisms are based mostly on a threshold over the prediction confidence of a pre-trained network. In contrast, SelectiveNet is trained to optimize both classification (or regression) and rejection simultaneously, end-to-end. The result is a deep neural network that is optimized over the covered domain. In our experiments, we show a consistently improved risk-coverage trade-off over several well-known classification and regression datasets, thus reaching new state-of-the-art results for deep selective classification.

Key facts. Cited by 333 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jan 2019.

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Citing papers per year: 2021: 29; 2022: 36; 2023: 35; 2024: 41; 2025: 35; 2026: 157.

333 papers.

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  1. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 596
  2. 2022Improving alignment of dialogue agents via targeted human judgementscited by 527
  3. 2022Teaching language models to support answers with verified quotescited by 290
  4. 2022Mitigating Neural Network Overconfidence with Logit Normalizationcited by 251
  5. 2021MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Spacecited by 233
  6. 2021DICE: Leveraging Sparsification for Out-of-Distribution Detectioncited by 166
  7. 2023Non-Parametric Outlier Synthesiscited by 129
  8. 2021Machine Learning with a Reject Option: A surveycited by 107
  9. 2022Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectlycited by 86
  10. 2025Universal Model Routing for Efficient LLM Inferencecited by 80
  11. 2021Provable Guarantees for Understanding Out-of-distribution Detectioncited by 78
  12. 2023Dream the Impossible: Outlier Imagination with Diffusion Modelscited by 77
38 more
  1. 2021Differentiable Learning Under Triagecited by 74
  2. 2022On the Relation between Sensitivity and Accuracy in In-context Learningcited by 71
  3. 2022Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settingscited by 66
  4. 2022MaskTune: Mitigating Spurious Correlations by Forcing to Explorecited by 66
  5. 2022Training Uncertainty-Aware Classifiers with Conformalized Deep Learningcited by 62
  6. 2023When Does Confidence-Based Cascade Deferral Suffice?cited by 58
  7. 2021DOCTOR: A Simple Method for Detecting Misclassification Errorscited by 52
  8. 2021Adversarial Example Detection for DNN Models: A Review and Experimental Comparisoncited by 51
  9. 2022A Call to Reflect on Evaluation Practices for Failure Detection in Image Classificationcited by 51
  10. 2024Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinationscited by 51
  11. 2022Open-environment Machine Learningcited by 48
  12. 2023Principled Approaches for Learning to Defer with Multiple Expertscited by 38
  13. 2026Building Production-Ready Probes For Geminicited by 38
  14. 2022Nonparametric Uncertainty Quantification for Single Deterministic Neural Networkcited by 36
  15. 2023Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithmscited by 36
  16. 2024How Does Unlabeled Data Provably Help Out-of-Distribution Detection?cited by 36
  17. 2023What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifierscited by 35
  18. 2021Selective Regression Under Fairness Criteriacited by 34
  19. 2023Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstentioncited by 32
  20. 2024Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box Vision-Language Models for Selective Visual Question Answeringcited by 31
  21. 2024Overcoming Common Flaws in the Evaluation of Selective Classification Systemscited by 31
  22. 2022Calibrated Selective Classificationcited by 30
  23. 2024Regression with Multi-Expert Deferralcited by 29
  24. 2022RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustnesscited by 27
  25. 2022Augmenting Softmax Information for Selective Classification with Out-of-Distribution Datacited by 27
  26. 2024Realizable H-Consistent and Bayes-Consistent Loss Functions for Learning to Defercited by 26
  27. 2022CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learningcited by 24
  28. 2023Improving Selective Visual Question Answering by Learning from Your Peerscited by 24
  29. 2021Taxonomy of Machine Learning Safety: A Survey and Primercited by 22
  30. 2022Towards Better Selective Classificationcited by 21
  31. 2022Selective Prediction via Training Dynamicscited by 20
  32. 2021Disrupting Deep Uncertainty Estimation Without Harming Accuracycited by 19
  33. 2024Uncertainty Quantification on Clinical Trial Outcome Predictioncited by 19
  34. 2022A Learning Based Hypothesis Test for Harmful Covariate Shiftcited by 18
  35. 2021Self-Adaptive Training: Bridging Supervised and Self-Supervised Learningcited by 17
  36. 2022Concept-based Explanations for Out-Of-Distribution Detectorscited by 16
  37. 2024Agreement-Based Cascading for Efficient Inferencecited by 16
  38. 2025Mastering Multiple-Expert Routing: Realizable H-Consistency and Strong Guarantees for Learning to Defercited by 16

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