beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data

Part of Advances in Neural Information Processing Systems 29 (NIPS 2016)

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Valentina Zantedeschi, Rémi Emonet, Marc Sebban


During the past few years, the machine learning community has paid attention to developping new methods for learning from weakly labeled data. This field covers different settings like semi-supervised learning, learning with label proportions, multi-instance learning, noise-tolerant learning, etc. This paper presents a generic framework to deal with these weakly labeled scenarios. We introduce the beta-risk as a generalized formulation of the standard empirical risk based on surrogate margin-based loss functions. This risk allows us to express the reliability on the labels and to derive different kinds of learning algorithms. We specifically focus on SVMs and propose a soft margin beta-svm algorithm which behaves better that the state of the art.