Learning SVMs from Sloppily Labeled Data

By Guillaume Stempfel, Liva Ralaivola. 2009

73 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Optimization & Training

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Citations in the corpus

Towards the Science of Security and Privacy in Machine Learning (2016, background): “Their approach does not impact the convexity of the optimization problem unlike previous attempts [78], [79], which reduces the impact of the defense mechanism on performance.”
SoK: Security and Privacy in Machine Learning (2018, background): “Their approach does not impact the convexity of the optimization problem unlike previous attempts [78], [79], which reduces the impact of the defense mechanism on performance.”
Support Vector Machines Under Adversarial Label Noise (2011, methodology): “…level” would be an interesting issue for future work, as well as considering our method in real adversarial problems like spam filtering and intrusion detection, and comparing it with other SVM implementations which are meant to be robust against label noise (e.g., Stempfel and Ralaivola, 2009).”
Loss factorization, weakly supervised learning and label noise robustness (2016, background): “…that requiring the strongest form of robustness –on any possible noisy sample– rules out most losses commonly used, and have drifted research focus on non-convex [Stempfel and Ralaivola, 2009, Masnadi-Shirazi et al., 2010, Ding and Vishwanathan, 2010] or linear losses [van Rooyen et al., 2015].”
Weakly supervised learning via statistical sufficiency (2016, background): “, 2015] or requires certain knowledge of the label corruption p(ỹ|y) [Stempfel and Ralaivola, 2009; Natarajan et al., 2013], with the noticeable exception of the linear “unhinged” loss [van Rooyen et al.”
Learning with Symmetric Label Noise: The Importance of Being Unhinged (2015, background): “The first approach has been pursued in a large body of work that embraces non-convex losses [Stempfel and Ralaivola, 2009, Masnadi-Shirazi et al., 2010, Ding and Vishwanathan, 2010, Denchev et al., 2012, Manwani and Sastry, 2013].”
Classification in the Presence of Label Noise: A Survey (2014, methodology): “A nonconvex loss is also used in [285] to produce label noise-tolerant SVMs without ﬁltering.”
Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach (2016, background): “In particular, we are interested in the design of corrected losses that are robust to label noise [38, 28, 30].”
A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning (2022, methodology): “Literature Model-protected Strategy Adversarial Setting Year Techniques Goals [4] cross-models data-driven cross-attacks UnT 2010 [33] logistic regression data-driven DM UnT 2014 [72] cross-models data-driven LM UnT 2015 [99] cross-models data-driven LM UnT 2018 [98] cross-models data-driven cross-a”