mlsanityData to 7 Oct 2026

Ranking with Abstention

arXiv 2307.02035 ↗First posted 5 Jul 2023cs.LG stat.MLcited by 27

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Abstract (arXiv)

We introduce a novel framework of ranking with abstention, where the learner can abstain from making prediction at some limited cost c. We present a extensive theoretical analysis of this framework including a series of H-consistency bounds for both the family of linear functions and that of neural networks with one hidden-layer. These theoretical guarantees are the state-of-the-art consistency guarantees in the literature, which are upper bounds on the target loss estimation error of a predictor in a hypothesis set H, expressed in terms of the surrogate loss estimation error of that predictor. We further argue that our proposed abstention methods are important when using common equicontinuous hypothesis sets in practice. We report the results of experiments illustrating the effectiveness of ranking with abstention.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 12 of its 58 references that are in the map. First posted on arXiv 5 Jul 2023.

Builds on

12 of 58 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  4. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  5. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  6. 2018Robustness May Be at Odds with Accuracycited by 873
  7. 2023Cross-Entropy Loss Functions: Theoretical Analysis and Applicationscited by 206
  8. 2021Calibration and Consistency of Adversarial Surrogate Lossescited by 47
  9. 2013One-Pass AUC Optimizationcited by 42
  10. 2023Revisiting Discriminative vs. Generative Classifiers: Theory and Implicationscited by 29
  11. 2021A Finer Calibration Analysis for Adversarial Robustnesscited by 26
  12. 2020Convex Calibrated Surrogates for the Multi-Label F-Measurecited by 18

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 11; 2025: 8; 2026: 6.

27 papers.

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  1. 2023Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithmscited by 36
  2. 2023Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstentioncited by 32
  3. 2024Regression with Multi-Expert Deferralcited by 29
  4. 2024Realizable H-Consistent and Bayes-Consistent Loss Functions for Learning to Defercited by 26
  5. 2024A Universal Growth Rate for Learning with Smooth Surrogate Lossescited by 24
  6. 2024Cardinality-Aware Set Prediction and Top-k Classificationcited by 23
  7. 2024Multi-Label Learning with Stronger Consistency Guaranteescited by 22
  8. 2024H-Consistency Guarantees for Regressioncited by 21
  9. 2024Enhanced H-Consistency Boundscited by 18
  10. 2025Mastering Multiple-Expert Routing: Realizable H-Consistency and Strong Guarantees for Learning to Defercited by 16
  11. 2024Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guaranteescited by 15
  12. 2025Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Datacited by 15
15 more
  1. 2025Budgeted Multiple-Expert Deferralcited by 11
  2. 2025Improved Balanced Classification with Theoretically Grounded Loss Functionscited by 9
  3. 2025Why Ask One When You Can Ask k? Learning-to-Defer to the Top-k Expertscited by 6
  4. 2025Principled Algorithms for Optimizing Generalized Metrics in Binary Classificationcited by 6
  5. 2024Top-k Classification and Cardinality-Aware Predictioncited by 5
  6. 2025Adversarial Robustness in One-Stage Learning-to-Defercited by 5
  7. 2026A Theoretical Framework for Modular Learning of Robust Generative Modelscited by 4
  8. 2026When More Experts Hurt: Underfitting in Multi-Expert Learning to Defercited by 2
  9. 2026Optimized Deferral for Imbalanced Settingscited by 2
  10. 2026Linear-Core Surrogates: Smooth Loss Functions with Linear Rates for Classification and Structured Predictioncited by 2
  11. 2024Towards Trustworthy Reranking: A Simple yet Effective Abstention Mechanismcited by 1
  12. 2024Asymptotically and Minimax Optimal Regret Bounds for Multi-Armed Bandits with Abstentioncited by 1
  13. 2025Bounded-Abstention Pairwise Learning to Rankcited by 0
  14. 2026Online Learning-to-Defer with Varying Expertscited by 0
  15. 2026Principled Algorithms for Optimizing Generalized Metrics in Multi-Label Learningcited by 0