Jul 16, 2020 · In this paper, we aim to bridge this gap by learning perturbation sets from data, in order to characterize real-world effects for robust ...
Jul 20, 2020 · To solve this problem, we propose a simple approach of learning a characterization of the perturbation set from data. At a high level, we learn ...
Jul 16, 2020 · Learning perturbation sets for robust machine learning. A repository that implements perturbation learning code, capable of learning ...
Oct 8, 2020 · In this paper, we aim to bridge this gap by learning perturbation sets from data, in order to characterize real-world effects for robust ...
Learning perturbation sets for robust machine learning. Eric Wong, Zico Kolter. Keywords: conditional variational autoencoder, adversarial examples, ...
May 3, 2021 · In this paper, we aim to bridge this gap by learning perturbation sets from data, in order to characterize real-world effects for robust ...
Jul 16, 2020 · This paper uses a conditional generator that defines the perturbation set over a constrained region of the latent space, and formulates ...
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Oct 15, 2023 · TL;DR: We introduce a setting for adversarial robust learning that interpolates between a known perturbation type and entirely unknown perturbation types by ...
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In many real-world settings exact perturbation sets to be used by an adversary are not plausibly available to a learner. While prior literature has studied ...
In this paper, we consider robust learning with respect to unknown perturbation sets U. ... Provably robust deep learning via adversarially trained smoothed ...