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Stochastic AUC Maximization with Deep Neural Networks

arXiv 1908.10831 ↗First posted 28 Aug 2019cs.LG math.OC stat.MLcited by 61

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Abstract (arXiv)

Stochastic AUC maximization has garnered an increasing interest due to better fit to imbalanced data classification. However, existing works are limited to stochastic AUC maximization with a linear predictive model, which restricts its predictive power when dealing with extremely complex data. In this paper, we consider stochastic AUC maximization problem with a deep neural network as the predictive model. Building on the saddle point reformulation of a surrogated loss of AUC, the problem can be cast into a it non-convex concave min-max problem. The main contribution made in this paper is to make stochastic AUC maximization more practical for deep neural networks and big data with theoretical insights as well. In particular, we propose to explore Polyak-Lojasiewicz (PL) condition that has been proved and observed in deep learning, which enables us to develop new stochastic algorithms with even faster convergence rate and more practical step size scheme. An AdaGrad-style algorithm is also analyzed under the PL condition with adaptive convergence rate. Our experimental results demonstrate the effectiveness of the proposed algorithms.

Key facts. Cited by 61 papers in the map (data through 13 Oct 2026). First posted on arXiv 28 Aug 2019.

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Citing papers per year: 2021: 12; 2022: 21; 2023: 16; 2024: 6; 2025: 5; 2026: 1.

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  1. 2022AUC Maximization in the Era of Big Data and AI: A Surveycited by 100
  2. 2021Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergencecited by 35
  3. 2021Learning with Multiclass AUC: Theory and Algorithmscited by 35
  4. 2021Randomized Stochastic Variance-Reduced Methods for Multi-Task Stochastic Bilevel Optimizationcited by 33
  5. 2022OpenAUC: Towards AUC-Oriented Open-Set Recognitioncited by 33
  6. 2022When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guaranteecited by 29
  7. 2022Differentially Private SGDA for Minimax Problemscited by 23
  8. 2021Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexitycited by 21
  9. 2021Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learningcited by 20
  10. 2022Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimizationcited by 20
  11. 2024AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentationcited by 18
  12. 2021Near Optimal Stochastic Algorithms for Finite-Sum Unbalanced Convex-Concave Minimax Optimizationcited by 16
38 more
  1. 2022Minimax AUC Fairness: Efficient Algorithm with Provable Convergencecited by 16
  2. 2022Optimizing Two-way Partial AUC with an End-to-end Frameworkcited by 15
  3. 2023LibAUC: A Deep Learning Library for X-Risk Optimizationcited by 15
  4. 2022Stability and Generalization for Markov Chain Stochastic Gradient Methodscited by 14
  5. 2021Unified Convergence Analysis for Adaptive Optimization with Moving Average Estimatorcited by 13
  6. 2025OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuningcited by 12
  7. 2022Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximizationcited by 11
  8. 2022Decentralized Stochastic Gradient Descent Ascent for Finite-Sum Minimax Problemscited by 11
  9. 2023Weakly Supervised AUC Optimization: A Unified Partial AUC Approachcited by 11
  10. 2023Solving a Class of Non-Convex Minimax Optimization in Federated Learningcited by 10
  11. 2024Fundamental Benefit of Alternating Updates in Minimax Optimizationcited by 9
  12. 2024An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothnesscited by 9
  13. 2022Algorithmic Foundations of Empirical X-risk Minimizationcited by 8
  14. 2022INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networkscited by 8
  15. 2022SGDA with shuffling: faster convergence for nonconvex-PL minimax optimizationcited by 8
  16. 2023Federated Compositional Deep AUC Maximizationcited by 8
  17. 2021Manifold Learning for Knowledge Discovery and Intelligent Inverse Design of Photonic Nanostructures: Breaking the Geometric Complexitycited by 6
  18. 2021Deep AUC Maximization for Medical Image Classification: Challenges and Opportunitiescited by 6
  19. 2022An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimizationcited by 6
  20. 2022AUC Maximization for Low-Resource Named Entity Recognitioncited by 6
  21. 2022Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithmcited by 5
  22. 2023Faster Stochastic Algorithms for Minimax Optimization under Polyak--Lojasiewicz Conditionscited by 5
  23. 2023Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimizationcited by 4
  24. 2024Single-Loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functionscited by 4
  25. 2022AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problemscited by 3
  26. 2023Efficient Fraud Detection Using Deep Boosting Decision Treescited by 3
  27. 2023Provable Multi-instance Deep AUC Maximization with Stochastic Poolingcited by 3
  28. 2023Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Miningcited by 3
  29. 2023DRAUC: An Instance-wise Distributionally Robust AUC Optimization Frameworkcited by 3
  30. 2021CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problemscited by 2
  31. 2022A Penalty-Based Method for Communication-Efficient Decentralized Bilevel Programmingcited by 2
  32. 2023Enhancing Personalized Ranking With Differentiable Group AUC Optimizationcited by 2
  33. 2024Accelerated Stochastic Min-Max Optimization Based on Bias-corrected Momentumcited by 2
  34. 2025Adaptive Algorithms with Sharp Convergence Rates for Stochastic Hierarchical Optimizationcited by 2
  35. 2025Federated Stochastic Minimax Optimization under Heavy-Tailed Noisescited by 2
  36. 2021Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twittercited by 1
  37. 2023A Log-linear Gradient Descent Algorithm for Unbalanced Binary Classification using the All Pairs Squared Hinge Losscited by 1
  38. 2023Ranking Regularization for Critical Rare Classes: Minimizing False Positives at a High True Positive Ratecited by 1

50 most cited of 61 All 61 (JSON)