Online AUC Maximization

By Peilin Zhao, Steven C. H. Hoi, Rong Jin, Tianbao Yang. 2011

240 citations recorded by the source corpus.

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Citations in the corpus

Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions (2016, neutral): “, Area Under the ROC Curve (AUC) maximization (Zhao et al., 2011), distance/similarity learning (Bellet et al.”
AUC Maximization in the Era of Big Data and AI: A Survey (2022, methodology): “In [36], multiple online AUC maximization algorithms including OAM [201] and OPAUC [47] are applied to the benchmark datasets for outlier/anomaly detection such as Webspam [163], Sensor Faults [114], and Malware App [205].”
Online Machine Learning in Big Data Streams (2018, background): “Although one deﬁnition of ROC AUC [82] is based on true and false positive rates, its online interpretation is described in [238].”
Distribution-Free One-Pass Learning (2017, methodology): “Some modern algorithms like OAM [2] approach, proposing to maximize online Area under ROC Curve (AUC) based on the idea of reservoir sampling, and achieving a solid regret bound by only storingOð ffiffiffiffi T p Þ instances, where T is the number of training samples.”
Collaborating between Local and Global Learning for Distributed Online Multiple Tasks (2015, methodology): “Online learning method [20, 36, 37] learns a set of data samples that arrive sequentially in time.”
A survey on concept drift adaptation (2014, background): “[Zliobaite 2011a], [Zhao et al. 2011], [Salganicoff 1993]”
Online Learning: A Comprehensive Survey (2018, methodology): “For example, the study in (Wang et al., 2012c) generalized online AUC maximization as online learning with general pairwise loss functions, and offered new generalization bounds for online AUC maximization algorithms similar to (Zhao et al., 2011b).”
Confidence Weighted Multitask Learning (2019, background): “In contrast to batch learning, which often suffers from expensive re-training costs whenever new training data come, online learning avoids re-training and learns incrementally from sequential data, which is much more efﬁcient (Hoi et al. 2018; Zhao et al. 2011).”