On the hardness of robust classification
WebIt is becoming increasingly important to understand the vulnerability of machine learning models to adversarial attacks. In this paper we study the feasibility of adversarially … WebComputational Hardness of PAC Learning Finally, we consider computational aspects of robust learning. Our focus is on two questions: computability and computational …
On the hardness of robust classification
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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: … Web30 de jul. de 2024 · On the Hardness of Robust Classification Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, and James Worrell University of Oxford August 20, 2024 Abstract It is becoming increasingly important to understand the vulnerability of machine-learning models to adversarial attacks.
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. WebOn the Hardness of Robust Classification The present paper is about robust learnability, and important problem for our ML community. The authors provide both theoretical and methodological contributions to address sample complexity and computational efficiency in the robust learning framework.
Web4 de fev. de 2024 · In this work, we extend their work in three directions. First, we demonstrate classification tasks where computationally efficient robust classification is impossible, even when computationally unbounded robust classifiers exist. For this, we rely on the existence of average-case hard functions. Second, we show hard-to-robustly-learn ... Web4 de fev. de 2024 · We continue the study of computational limitations in learning robust classifiers, following the recent work of Bubeck, Lee, Price and Razenshteyn. First, we demonstrate classification tasks where computationally efficient robust classifiers do not exist, even when computationally unbounded robust classifiers do. We rely on the …
Web27 de fev. de 2024 · We rely on the hardness of decoding problems with preprocessing on codes and lattices. Second, we show hard-to-robustly-learn classification tasks *in the large-perturbation regime*. Namely, we show that even though an efficient classifier that is very robust (namely, tolerant to large perturbations) exists, it is computationally hard to …
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