The DOI matches its Crossref record; 94 of the 94 references it cites were found in a registry or an index.
AUC Maximization in the Era of Big Data and AI: A Survey
Open accessAbstract
Area under the ROC curve, a.k.a. AUC, is a measure of choice for assessing the performance of a classifier for imbalanced data. AUC maximization refers to a learning paradigm that learns a predictive model by directly maximizing its AUC score. It has been studied for more than two decades dating back to late 90s, and a huge amount of work has been devoted to AUC maximization since then. Recently, stochastic AUC maximization for big data and deep AUC maximization (DAM) for deep learning have received increasing attention and yielded dramatic impact for solving real-world problems. However, to the best our knowledge, there is no comprehensive survey of related works for AUC maximization. This article aims to address the gap by reviewing the literature in the past two decades. We not only give a holistic view of the literature but also present detailed explanations and comparisons of different papers from formulations to algorithms and theoretical guarantees. We also identify and discuss remaining and emerging issues for DAM and provide suggestions on topics for future work.
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How Can Recommender Systems Benefit from Large Language Models: A Survey
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Squidiff: predicting cellular development and responses to perturbations using a diffusion model
He, S., Zhu, Y., Tavakol, D. N., Ye, H., Lao, Y.-H., Zhu, Z., Xu, C., Chauhan, S., Garty, G., Tomer, R., et al. (2026) · Nature Methods · doi:10.1038/s41592-025-02877-y · Cited by 38
Kasim, S., Amir Rudin, P. N. F., Malek, S., Ibrahim, N., Kiew, X. N., Nasir, N. M., Ibrahim, K. S., & Raja Shariff, R. E. (2025) · PLOS One · doi:10.1371/journal.pone.0323949 · Cited by 3
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Metadata
- Authors
- Yang, T., & Ying, Y.
- Venue
- ACM Computing Surveys
- Published
- 2022-12-23
- Volume
- 55
- Issue
- 8
- Pages
- 1-37
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W4226211505
- Cited by
- 323
- References
- 94
- FWCI
- 19.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
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