Web27 de mai. de 2024 · To mitigate this problem, a series of robust learning algorithms have been proposed. However, although the... Skip to main content. ... for binary classification problems with well-separated data, we show that, ... our results reveal that the hardness of robust generalization may stem from the expressive power of practical models ... WebFinally, we provide a simple proof of the computational hardness of robust learning on the boolean hypercube. Unlike previous results of this nature, our result does not rely on another computational model (e.g. the statistical query model) nor on any hardness assumption other than the existence of a hard learning problem in the PAC framework.
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Web4 de fev. de 2024 · We show two such classification tasks in the large-perturbation regime: the first relies on the existence of one-way functions, a minimal assumption in cryptography; and the second on the hardness ... Web6 de set. de 2024 · On the Hardness of Robust Classification. Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell. 06 Sept 2024, 20:42 (modified: 05 Nov … inara herdman
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http://export.arxiv.org/abs/1909.05822 Web19 de out. de 2024 · Abstract. Motivated by the fact that there may be inaccuracies in features and labels of training data, we apply robust optimization techniques to study in a principled way the uncertainty in data features and labels in classification problems and obtain robust formulations for the three most widely used classification methods: … Web1. Novelty and Significance: The paper mostly presents some impossibility results on robust binary classification under adversarial perturbation, which could be of independent interest for a mathematical perspective. However it has not been made clear how do these impossibility results have any impact from a practical point of view. incheon driving license office