mlsanityData to 14 Oct 2026

One-Pass AUC Optimization

arXiv 1305.1363 ↗First posted 7 May 2013cs.LGcited by 42

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Abstract (arXiv)

AUC is an important performance measure and many algorithms have been devoted to AUC optimization, mostly by minimizing a surrogate convex loss on a training data set. In this work, we focus on one-pass AUC optimization that requires only going through the training data once without storing the entire training dataset, where conventional online learning algorithms cannot be applied directly because AUC is measured by a sum of losses defined over pairs of instances from different classes. We develop a regression-based algorithm which only needs to maintain the first and second order statistics of training data in memory, resulting a storage requirement independent from the size of training data. To efficiently handle high dimensional data, we develop a randomized algorithm that approximates the covariance matrices by low rank matrices. We verify, both theoretically and empirically, the effectiveness of the proposed algorithm.

Key facts. Cited by 42 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 May 2013.

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Citing papers per year: 2021: 9; 2022: 18; 2023: 10; 2024: 2; 2025: 2; 2026: 1.

42 papers.

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  1. 2022AUC Maximization in the Era of Big Data and AI: A Surveycited by 100
  2. 2021Stability and Generalization of Stochastic Gradient Methods for Minimax Problemscited by 36
  3. 2021Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergencecited by 35
  4. 2021Learning with Multiclass AUC: Theory and Algorithmscited by 35
  5. 2023Ranking with Abstentioncited by 27
  6. 2022Rank-based Decomposable Losses in Machine Learning: A Surveycited by 26
  7. 2022Differentially Private SGDA for Minimax Problemscited by 23
  8. 2021Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learningcited by 20
  9. 2023Embedding in Recommender Systems: A Surveycited by 20
  10. 2021Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classescited by 19
  11. 2024AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentationcited by 18
  12. 2022Minimax AUC Fairness: Efficient Algorithm with Provable Convergencecited by 16
30 more
  1. 2022Optimizing Two-way Partial AUC with an End-to-end Frameworkcited by 15
  2. 2021Momentum Accelerates the Convergence of Stochastic AUPRC Maximizationcited by 14
  3. 2022Rethinking Collaborative Metric Learning: Toward an Efficient Alternative without Negative Samplingcited by 14
  4. 2022Stability and Generalization for Markov Chain Stochastic Gradient Methodscited by 14
  5. 2022ORFit: One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squarescited by 12
  6. 2022FeDXL: Provable Federated Learning for Deep X-Risk Optimizationcited by 11
  7. 2023Weakly Supervised AUC Optimization: A Unified Partial AUC Approachcited by 11
  8. 2022Benchmarking Deep AUROC Optimization: Loss Functions and Algorithmic Choicescited by 10
  9. 2022Optimizing Partial Area Under the Top-k Curve: Theory and Practicecited by 9
  10. 2021Deep AUC Maximization for Medical Image Classification: Challenges and Opportunitiescited by 6
  11. 2022AUC Maximization for Low-Resource Named Entity Recognitioncited by 6
  12. 2022AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problemscited by 3
  13. 2023Provable Multi-instance Deep AUC Maximization with Stochastic Poolingcited by 3
  14. 2022Stability and Generalization of Differentially Private Minimax Problemscited by 2
  15. 2022Pairwise Learning via Stagewise Training in Proximal Settingcited by 2
  16. 2023Enhancing Personalized Ranking With Differentiable Group AUC Optimizationcited by 2
  17. 2023Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?cited by 2
  18. 2022Evolutionary Multitasking AUC Optimizationcited by 1
  19. 2022Differentially Private Stochastic Gradient Descent with Low-Noisecited by 1
  20. 2023Variance Reduced Online Gradient Descent for Kernelized Pairwise Learning with Limited Memorycited by 1
  21. 2021Estimating the Arc Length of the Optimal ROC Curve and Lower Bounding the Maximal AUCcited by 0
  22. 2021Learning Rates for Nonconvex Pairwise Learningcited by 0
  23. 2022Balanced Self-Paced Learning for AUC Maximizationcited by 0
  24. 2023Domain Knowledge integrated for Blast Furnace Classifier Designcited by 0
  25. 2023AUC Optimization from Multiple Unlabeled Datasetscited by 0
  26. 2023Does it pay to optimize AUC?cited by 0
  27. 2024Limited Memory Online Gradient Descent for Kernelized Pairwise Learning with Dynamic Averagingcited by 0
  28. 2025Stability-based Generalization Analysis of Randomized Coordinate Descent for Pairwise Learningcited by 0
  29. 2025Online AUC Optimization Based on Second-order Surrogate Losscited by 0
  30. 2026Communication-Efficient Federated AUC Maximization with Cyclic Client Participationcited by 0