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SupplementaryMaterialforLipschitz-Certifiable TrainingwithaTightOuterBound

Neural Information Processing Systems

We want to provep is a local minimum of(11), then since (11) is a convex optimization, we can prove that p is the global optimum. We consider a closed local areaB(p,ฮด > 0) such that for any q B(p,ฮด), q 0 and we can ignore the box constraint forql for l Jc. We call a local optimal solution of(11) in B(p,ฮด) as p . Moreover, if kp k < 1, then we can further extendp [Jc] to produce a larger inner product withv, and this contradicts the assumption. After propagating a ballB2(ยต,ฯ) through a ReLU layer, we can estimate the propagated outer bound with anew ballB2(ยต+,ฯ)whereยต+ = max(ยต,0). However, the true image ReLU(B2(ยต,ฯ)) has no negative elements.




2c23b3c72127e15fedc276722faee927-Paper-Conference.pdf

Neural Information Processing Systems

Aweakness toexisting approaches isthattheyignore thefactthatadversarial example generation is often a sequential task where multiple similar problems are being solved in a row. That is, one has access to a large number of "normal" examples each of which should be perturbed to elicit






Reinforcement Learningwith Feedback Graphs

Neural Information Processing Systems

Another where, thegoal recommend improvethe10]. Theworkof YMreceivedfunding fromthe European Research Council (ERC) underthe European Union' s Horizon 2020 research andinnovationprogram (grantagreement No. 882396), andbythe Israel Science Foundation (grant number 993/17).