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A natural question for these settings is whether or not we can make classifiers computationally robust to polynomial-time attacks. ... Making learners robust to adversarial perturbation at test time (i.e., evasion attacks finding adversarial examples) or training time (i.e., data poisoning attacks) has emerged as a challenging task. ... computational hardness. ...dblp:conf/alt/MahloujifarM19 fatcat:gjif3ko5zrcifgeykl3gcm2ibm
Our developed theory reveals the nontrivial effect of overparametrization on robustness and indicates that for adversarially trained random features models, high overparametrization can hurt robust generalization ... Despite the remarkable success of deep learning architectures in the overparametrized regime, it is also well known that these models are highly vulnerable to small adversarial perturbations in their inputs ... Hassani is supported by the NSF CAREER award, AFOSR YIP, the Intel Rising Star award, as well as the AI Institute for Learning-Enabled Optimization at Scale (TILOS). A. ...arXiv:2201.05149v1 fatcat:exxtlpgfqvajdgf4kxdc5jtrtm